U.S. patent application number 17/388519 was filed with the patent office on 2022-06-16 for interactive diagnostics for evaluating designs for measurement systems analysis.
The applicant listed for this patent is SAS Institute Inc.. Invention is credited to Bradley Allen Jones, Caleb Bridges King, Ryan Adam Lekivetz, Joseph Albert Morgan.
Application Number | 20220187169 17/388519 |
Document ID | / |
Family ID | 1000006374528 |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220187169 |
Kind Code |
A1 |
King; Caleb Bridges ; et
al. |
June 16, 2022 |
Interactive Diagnostics for Evaluating Designs for Measurement
Systems Analysis
Abstract
A computing system receives a request for computer-generated
likelihood(s) for candidate evaluations of an industrial product
set according to a measurement system analysis (MSA). The MSA
comprises tests for evaluating, according to a measurement
standard, the industrial product set. The request indicates a
metric set representing metric(s) each quantifying an estimate of
contribution to variation in evaluating the industrial product set
according to the MSA. The system generates a design comprising a
respective input set for each test of the MSA. The respective input
set comprises setting(s) for conducting a test of the MSA. The
design is designed to isolate candidate sources for contributing to
the variation in evaluating the industrial product set according to
the MSA. The system (e.g., prior to the MSA) outputs, based on the
metric set and the design, the computer-generated likelihood(s) for
the candidate evaluations of the industrial product set according
to the MSA.
Inventors: |
King; Caleb Bridges; (Cary,
NC) ; Morgan; Joseph Albert; (Raleigh, NC) ;
Lekivetz; Ryan Adam; (Cary, NC) ; Jones; Bradley
Allen; (Cary, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SAS Institute Inc. |
Cary |
NC |
US |
|
|
Family ID: |
1000006374528 |
Appl. No.: |
17/388519 |
Filed: |
July 29, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
63123628 |
Dec 10, 2020 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01M 99/00 20130101;
G06N 5/04 20130101 |
International
Class: |
G01M 99/00 20060101
G01M099/00; G06N 5/04 20060101 G06N005/04 |
Claims
1. A computer-program product tangibly embodied in a non-transitory
machine-readable storage medium, the computer-program product
including instructions operable to cause a computing system to:
prior to a measurement system analysis, receive a request for one
or more computer-generated likelihoods for respective candidate
evaluations of an industrial product set according to the
measurement system analysis, wherein the measurement system
analysis comprises measurement tests for evaluating, according to a
measurement standard, the industrial product set comprising one or
more industrial products; wherein each measurement test of the
measurement tests has a respective setting for each member of a
factor set comprising one or more factors of the measurement system
analysis; wherein the request indicates a metric set representing
one or more metrics each quantifying, prior to the measurement
system analysis, an estimate of contribution to variation in
evaluating the industrial product set according to the measurement
system analysis; and wherein the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set comprise one or more of: a likelihood of
classification into one of multiple groups for the industrial
product set according to the measurement standard; and a likelihood
of going beyond a threshold related to the measurement standard;
prior to the measurement system analysis, generate an input design
comprising a respective input set for each respective measurement
test of the measurement system analysis, wherein the respective
input set comprises one or more settings for conducting the
respective measurement test of the measurement system analysis; and
wherein the input design is designed to isolate candidate sources
for contributing to the variation in evaluating the industrial
product set according to the measurement system analysis; and prior
to the measurement system analysis, output, based on the metric set
and the input design, the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set according to the measurement system
analysis.
2. (canceled)
3. The computer-program product of claim 1, wherein the
instructions are operable to cause the computing system to output
the one or more computer-generated likelihoods for the respective
candidate evaluations of the industrial product set by: determining
an interclass correlation indicating a proportion of a total
variation in operation of the industrial product set that is
attributable to a member of the industrial product set; and
generating, based on the interclass correlation, an Evaluating the
Measurement Process (EMP) classification.
4. A computer-program product tangibly embodied in a non-transitory
machine-readable storage medium, the computer-program product
including instructions operable to cause a computing system to:
prior to a measurement system analysis, receive a request for one
or more computer-generated likelihoods for respective candidate
evaluations of an industrial product set according to the
measurement system analysis, wherein the measurement system
analysis comprises measurement tests for evaluating, according to a
measurement standard, the industrial product set comprising one or
more industrial products; wherein each measurement test of the
measurement tests has a respective setting for each member of a
factor set comprising one or more factors of the measurement system
analysis; and wherein the request indicates a metric set
representing one or more metrics each quantifying, prior to the
measurement system analysis, an estimate of contribution to
variation in evaluating the industrial product set according to the
measurement system analysis; prior to the measurement system
analysis, generate an input design comprising a respective input
set for each respective measurement test of the measurement system
analysis, wherein the respective input set comprises one or more
settings for conducting the respective measurement test of the
measurement system analysis; and wherein the input design is
designed to isolate candidate sources for contributing to the
variation in evaluating the industrial product set according to the
measurement system analysis; receive an indication from a user to
include or exclude one or more of: bias of a respective one of
multiple factors in the measurement system analysis; and
interaction of multiple factors in the measurement system analysis;
and prior to the measurement system analysis, output, accounting
for the metric set, the input design, and the indication, the one
or more computer-generated likelihoods for the respective candidate
evaluations of the industrial product set according to the
measurement system analysis.
5. A computer-program product tangibly embodied in a non-transitory
machine-readable storage medium, the computer-program product
including instructions operable to cause a computing system to:
prior to a measurement system analysis, receive a request for one
or more computer-generated likelihoods for respective candidate
evaluations of an industrial product set according to the
measurement system analysis, wherein the measurement system
analysis comprises measurement tests for evaluating, according to a
measurement standard, the industrial product set comprising one or
more industrial products; wherein each measurement test of the
measurement tests has a respective setting for each member of a
factor set comprising one or more factors of the measurement system
analysis; and wherein the request indicates a metric set
representing one or more metrics each quantifying, prior to the
measurement system analysis, an estimate of contribution to
variation in evaluating the industrial product set according to the
measurement system analysis; prior to the measurement system
analysis, generate an input design comprising a respective input
set for each respective measurement test of the measurement system
analysis, wherein the respective input set comprises one or more
settings for conducting the respective measurement test of the
measurement system analysis; and wherein the input design is
designed to isolate candidate sources for contributing to the
variation in evaluating the industrial product set according to the
measurement system analysis; and prior to the measurement system
analysis, output, based on the metric set and the input design, the
one or more computer-generated likelihoods for the respective
candidate evaluations of the industrial product set according to
the measurement system analysis by displaying in a graphical user
interface an initial output for the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set and the metric set; receive a user
indication to change one or more metrics of the metric set; and
update the graphical user interface to display an updated output
for the one or more computer-generated likelihoods for the
respective candidate evaluations of the industrial product set,
wherein the updated output accounts for the user indication.
6. A computer-program product tangibly embodied in a non-transitory
machine-readable storage medium, the computer-program product
including instructions operable to cause a computing system to:
prior to a measurement system analysis, receive a request for one
or more computer-generated likelihoods for respective candidate
evaluations of an industrial product set according to the
measurement system analysis, wherein the measurement system
analysis comprises measurement tests for evaluating, according to a
measurement standard, the industrial product set comprising one or
more industrial products; wherein each measurement test of the
measurement tests has a respective setting for each member of a
factor set comprising one or more factors of the measurement system
analysis; and wherein the request indicates a metric set
representing one or more metrics each quantifying, prior to the
measurement system analysis, an estimate of contribution to
variation in evaluating the industrial product set according to the
measurement system analysis; prior to the measurement system
analysis, generate an input design comprising a respective input
set for each respective measurement test of the measurement system
analysis, wherein the respective input set comprises one or more
settings for conducting the respective measurement test of the
measurement system analysis; and wherein the input design is
designed to isolate candidate sources for contributing to the
variation in evaluating the industrial product set according to the
measurement system analysis; prior to the measurement system
analysis, output, based on the metric set and the input design, the
one or more computer-generated likelihoods for the respective
candidate evaluations of the industrial product set according to
the measurement system analysis; and evaluate the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set according to the
measurement system analysis by: simulating results for the
simulation of the measurement system analysis; and displaying in a
graphical user interface one or more statistics or graphs related
to the simulation.
7. A computer-program product tangibly embodied in a non-transitory
machine-readable storage medium, the computer-program product
including instructions operable to cause a computing system to:
prior to a measurement system analysis, receive a request for one
or more computer-generated likelihoods for respective candidate
evaluations of an industrial product set according to the
measurement system analysis, wherein the measurement system
analysis comprises measurement tests for evaluating, according to a
measurement standard, the industrial product set comprising one or
more industrial products; wherein each measurement test of the
measurement tests has a respective setting for each member of a
factor set comprising one or more factors of the measurement system
analysis; wherein the request indicates a metric set representing
one or more metrics each quantifying, prior to the measurement
system analysis, an estimate of contribution to variation in
evaluating the industrial product set according to the measurement
system analysis; and wherein the metric set comprises one or more
of: an assumed variance for an operator set comprising one or more
operators measuring, in the measurement system analysis, at least
one of the industrial product set; an assumed variance for a tool
set comprising one or more measurement tools for measuring, in the
measurement system analysis, at least one member of the industrial
product set; and an assumed variance for operation of the
industrial product set in the measurement system analysis; prior to
the measurement system analysis, generate an input design
comprising a respective input set for each respective measurement
test of the measurement system analysis, wherein the respective
input set comprises one or more settings for conducting the
respective measurement test of the measurement system analysis; and
wherein the input design is designed to isolate candidate sources
for contributing to the variation in evaluating the industrial
product set according to the measurement system analysis; prior to
the measurement system analysis, output, based on the metric set
and the input design, the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set according to the measurement system
analysis.
8. The computer-program product of claim 7, wherein the
instructions are operable to cause the computing system to output
the one or more computer-generated likelihoods for the respective
candidate evaluations of the industrial product set by displaying
in a graphical user interface estimate statistics for: proportion
of the variation attributable to the operator set; proportion of
the variation attributable to the tool set; and proportion of the
variation attributable to the operation of the industrial product
set.
9. The computer-program product of claim 7, wherein the
instructions are operable to cause the computing system to: further
receive a user indication to include an operator factor in the
measurement system analysis indicating multiple operator
characteristics in the operator set, wherein the metric set
indicates the assumed variance for the operator set; and generate
the input design by generating respective inputs associated with
each of multiple operator characteristics of the operator set in
the measurement system analysis; and output, accounting for the
assumed variance for the operator set, the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set.
10. The computer-program product of claim 7, wherein the
instructions are operable to cause the computing system to: further
receive a user indication to include a gauge factor in the
measurement system analysis indicating multiple measurement tools
of the tool set, wherein the metric set indicates the assumed
variance for the tool set; generate the input design by generating
respective inputs associated with each of multiple measurement
tools in the measurement system analysis; and output, accounting
for the assumed variance for the tool set, the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set.
11. The computer-program product of claim 7, wherein the
instructions are operable to cause the computing system to: further
receive a user indication to include a part factor in the
measurement system analysis indicating multiple industrial products
of a same type in the industrial product set, wherein the metric
set indicates the assumed variance for the operation of the
industrial product set; generate the input design by generating
respective inputs associated with each of multiple industrial
products of the industrial product set in the measurement system
analysis; and output, accounting for the assumed variance for the
industrial product set, the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set.
12. The computer-program product of claim 1, wherein the
instructions are operable to cause the computing system to further
receive a user indication to include multiple factors in the
measurement system analysis; wherein the metric set indicates an
assumed variance for an interaction of one of more of the multiple
factors in the measurement system analysis; and wherein the input
design isolates the interaction of the one of more of the multiple
factors in the measurement system analysis.
13. The computer-program product of claim 7, wherein the metric set
comprises multiple metrics; wherein a respective one of the
multiple metrics indicates: a first assumed variance for the
operator set; a second assumed variance for the tool set; and a
third assumed variance for the operation of the industrial product
set in the measurement system analysis; and wherein the
instructions are operable to cause the computing system to output,
accounting for the first assumed variance, the second assumed
variance, and the third assumed variance, one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set.
14. The computer-program product of claim 1, wherein the
instructions are operable to cause the computing system to: further
receive a user indication of a restriction on generating the input
design that restricts an ordering for the measurement tests in the
measurement system analysis; generate the input design based on the
restriction to accommodate the ordering; and output, based on the
restriction, the one or more computer-generated likelihoods for the
respective candidate evaluations of the industrial product set
according to the measurement system analysis.
15. The computer-program product of claim 14, wherein the user
indication of the restriction indicates to restrict the ordering to
indicate an ordering for groups of the measurements tests for
respective members of a component of a measurement system analyzed
by the measurement system analysis; and wherein the instructions
are operable to cause the computing system to generate the input
design to indicate the groups of the measurement tests for the
respective members of the component of the measurement system.
16. The computer-program product of claim 1, wherein the
measurement system analysis comprises multiple studies with
replicated test conditions in each of the multiple studies; wherein
the request further indicates an amount of the multiple studies of
the industrial product set for the measurement system analysis;
wherein the user indication of the restriction indicates a
restriction to group measurement tests of a respective individual
study of the multiple studies in the ordering; and wherein the
instructions are operable to cause the computing system to generate
the input design to indicate groups of the measurement tests
grouped in the respective individual study of the multiple
studies.
17. A computer-program product tangibly embodied in a
non-transitory machine-readable storage medium, the
computer-program product including instructions operable to cause a
computing system to: prior to a measurement system analysis,
receive a request for one or more computer-generated likelihoods
for respective candidate evaluations of an industrial product set
according to the measurement system analysis, wherein the
measurement system analysis comprises measurement tests for
evaluating, according to a measurement standard, the industrial
product set comprising one or more industrial products; wherein
each measurement test of the measurement tests has a respective
setting for each member of a factor set comprising one or more
factors of the measurement system analysis; wherein the request
indicates a metric set representing one or more metrics each
quantifying, prior to the measurement system analysis, an estimate
of contribution to variation in evaluating the industrial product
set according to the measurement system analysis; and wherein the
measurement system analysis is: a destructive test for the
industrial product set such that a member of the industrial product
set is destroyed in testing according to the measurement system
analysis; or a non-destructive test for the industrial product set
such that a member of the industrial product set is reused in
testing according to the measurement system analysis; prior to the
measurement system analysis, generate an input design comprising a
respective input set for each respective measurement test of the
measurement system analysis by generating respective conditions for
testing each member of the industrial product set for multiple
factors for the measurement system analysis, wherein the respective
input set comprises one or more settings for conducting the
respective measurement test of the measurement system analysis;
wherein the input design is designed to isolate candidate sources
for contributing to the variation in evaluating the industrial
product set according to the measurement system analysis; and
wherein the input design indicates the respective conditions; and
prior to the measurement system analysis, output, based on the
metric set and the input design, the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set according to the measurement system
analysis.
18. The computer-program product of claim 17, wherein the
measurement system analysis is the non-destructive test for the
industrial product set such that the member of the industrial
product set is reused in testing according to the measurement
system analysis; wherein the request indicates an amount of reuse
of members of the industrial product set for testing; and wherein
the input design comprises at least two different sets of
conditions for each member of the industrial product set.
19. The computer-program product of claim 1, wherein the
instructions are operable to cause the computing system to:
generate the input design for the measurement system analysis by
including inputs for one or more blocking factors that are external
to a measurement procedure according to the measurement system
analysis; and output, accounting for an impact of the one or more
blocking factors, the one or more computer-generated likelihoods
for the respective candidate evaluations of the industrial product
set.
20. A computer-implemented method comprising: prior to a
measurement system analysis, receiving a request for one or more
computer-generated likelihoods for respective candidate evaluations
of an industrial product set according to the measurement system
analysis, wherein the measurement system analysis comprises
measurement tests for evaluating, according to a measurement
standard, the industrial product set comprising one or more
industrial products; wherein each measurement test of the
measurement tests has a respective setting for each member of a
factor set comprising one or more factors of the measurement system
analysis; wherein the request indicates a metric set representing
one or more metrics each quantifying, prior to the measurement
system analysis, an estimate of contribution to variation in
evaluating the industrial product set according to the measurement
system analysis; and wherein the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set comprise one or more of: a likelihood of
classification into one of multiple groups for the industrial
product set according to the measurement standard; and a likelihood
of going beyond a threshold related to the measurement standard;
prior to the measurement system analysis, generating an input
design comprising a respective input set for each respective
measurement test of the measurement system analysis, wherein the
respective input set comprises one or more settings for conducting
the respective measurement test of the measurement system analysis;
and wherein the input design is designed to isolate candidate
sources for contributing to the variation in evaluating the
industrial product set according to the measurement system
analysis; and prior to the measurement system analysis, outputting,
based on the metric set and the input design, the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set according to the
measurement system analysis.
21. (canceled)
22. The computer-implemented method of claim 20, wherein the
outputting the one or more computer-generated likelihoods for the
respective candidate evaluations of the industrial product set
comprises: determining an interclass correlation indicating a
proportion of a total variation in operation of the industrial
product set that is attributable to a member of the industrial
product set; and generating, based on the interclass correlation,
an Evaluating the Measurement Process (EMP) classification.
23. The computer-implemented method of claim 20, wherein the
outputting the one or more computer-generated likelihoods for the
respective candidate evaluations of the industrial product set
comprises displaying in a graphical user interface an initial
output for the one or more computer-generated likelihoods for the
respective candidate evaluations of the industrial product set and
the metric set; wherein the computer-implemented method further
comprises: receiving a user indication to change one or more
metrics of the metric set; and updating the graphical user
interface to display an updated output for the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set, wherein the updated
output accounts for the user indication.
24. The computer-implemented method of claim 20, wherein the
computer-implemented method further comprises evaluating the one or
more computer-generated likelihoods for the respective candidate
evaluations of the industrial product set according to the
measurement system analysis by: simulating results for the
simulation of the measurement system analysis; and displaying in a
graphical user interface one or more statistics or graphs related
to the simulation.
25. The computer-implemented method of claim 20, wherein the metric
set comprises one or more of: an assumed variance for an operator
set comprising one or more operators measuring, in the measurement
system analysis, at least one of the industrial product set; an
assumed variance for a tool set comprising one or more measurement
tools for measuring, in the measurement system analysis, at least
one member of the industrial product set; and an assumed variance
for operation of the industrial product set in the measurement
system analysis.
26. The computer-implemented method of claim 20, wherein the
computer-implemented method further comprises receiving a user
indication to include an operator factor in the measurement system
analysis indicating multiple operator characteristics in an
operator set, wherein the metric set indicates an assumed variance
for the operator set comprising one or more operators measuring, in
the measurement system analysis, at least one of the industrial
product set; and wherein the generating the input design comprises
generating respective inputs associated with each of multiple
operator characteristics of the operator set in the measurement
system analysis; and wherein the outputting the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set comprises outputting,
accounting for the assumed variance for the operator set, the one
or more computer-generated likelihoods for the respective candidate
evaluations of the industrial product set.
27. The computer-implemented method of claim 20, wherein the
computer-implemented method further comprises receiving a user
indication to include a gauge factor in the measurement system
analysis indicating multiple measurement tools of a tool set,
wherein the metric set indicates an assumed variance for the tool
set comprising one or more measurement tools for measuring, in the
measurement system analysis, at least one member of the industrial
product set; and wherein the generating the input design comprises
generating respective inputs associated with each of multiple
measurement tools in the measurement system analysis; and wherein
the outputting the one or more computer-generated likelihoods for
the respective candidate evaluations of the industrial product set
comprises outputting, accounting for the assumed variance for the
tool set, the one or more computer-generated likelihoods for the
respective candidate evaluations of the industrial product set.
28. The computer-implemented method of claim 20, wherein the
computer-implemented method further comprises receiving a user
indication to include a part factor in the measurement system
analysis indicating multiple industrial products of a same type in
the industrial product set, wherein the metric set indicates an
assumed variance for the industrial product set; wherein the
generating the input design comprises generating respective inputs
associated with each of multiple industrial products of the
industrial product set in the measurement system analysis; and
wherein the outputting the one or more computer-generated
likelihoods comprises for the respective candidate evaluations of
the industrial product set outputting, accounting for the assumed
variance for the industrial product set, the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set.
29. The computer-implemented method of claim 20, wherein the
computer-implemented method further comprises receiving a user
indication of a restriction on generating the input design that
restricts an ordering for the measurement tests in the measurement
system analysis; wherein the generating the input design comprises
generating the input design based on the restriction to accommodate
the ordering; and wherein the outputting the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set comprises outputting,
based on the restriction, the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set according to the measurement system
analysis.
30. A computing device comprising processor and memory, the memory
containing instructions executable by the processor wherein the
computing device is configured to: prior to a measurement system
analysis, receive a request for one or more computer-generated
likelihoods for respective candidate evaluations of an industrial
product set according to the measurement system analysis, wherein
the measurement system analysis comprises measurement tests for
evaluating, according to a measurement standard, the industrial
product set comprising one or more industrial products; wherein
each measurement test of the measurement tests has a respective
setting for each member of a factor set comprising one or more
factors of the measurement system analysis; wherein the request
indicates a metric set representing one or more metrics each
quantifying, prior to the measurement system analysis, an estimate
of contribution to variation in evaluating the industrial product
set according to the measurement system analysis; and wherein the
one or more computer-generated likelihoods for the respective
candidate evaluations of the industrial product set comprise one or
more of: a likelihood of classification into one of multiple groups
for the industrial product set according to the measurement
standard; and a likelihood of going beyond a threshold related to
the measurement standard; prior to the measurement system analysis,
generate an input design comprising a respective input set for each
respective measurement test of the measurement system analysis,
wherein the respective input set comprises one or more settings for
conducting the respective measurement test of the measurement
system analysis; and wherein the input design is designed to
isolate candidate sources for contributing to the variation in
evaluating the industrial product set according to the measurement
system analysis; and prior to the measurement system analysis,
output, based on the metric set and the input design, the one or
more computer-generated likelihoods for the respective candidate
evaluations of the industrial product set according to the
measurement system analysis.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of, and priority based
on, 35 U.S.C. .sctn. 119 to U.S. Provisional Application No.
63/123,628, filed Dec. 10, 2020, the disclosures of each of which
are incorporated herein by reference in their entirety.
BACKGROUND
[0002] Industrial products such as mechanical or electrical system
components or substrates can be prone to variations when
manufacturing large quantities. Variations can be problematic if,
for instance, the industrial products should be within certain
tolerances such as for safety (e.g., automotive or airplane
components) or simply to fit together well (e.g., toy snap together
bricks). Variations may come from many sources such as the part
itself or the measurement process for measuring the variation.
Industrial parts or their measurement process can be classified
based on their likelihood of meeting certain measurement standards
given possible variations. Classifications or evaluations for
industrial products can consider variations that come not only from
a product itself, but from the measurement process such as the
measurement tools, measurement operators, and measurement
techniques. A measurement system analysis (MSA) can be used to
evaluate a measurement process. For instance, measurement tests can
be designed and executed on a set of industrial parts for an
Evaluating the Measurement Process (EMP) classification, which can
be used to assess consistency, reproducibility, or repeatability of
a measurement system.
SUMMARY
[0003] In an example embodiment, a computer-program product
tangibly embodied in a non-transitory machine-readable storage
medium is provided. The computer-program product includes
instructions to cause a computing system to, prior to a measurement
system analysis, receive a request for one or more
computer-generated likelihoods for respective candidate evaluations
of an industrial product set according to the measurement system
analysis. The measurement system analysis comprises measurement
tests for evaluating, according to a measurement standard, the
industrial product set comprising one or more industrial products.
Each measurement test of the measurement tests has a respective
setting for each member of a factor set comprising one or more
factors of the measurement system analysis. The request indicates a
metric set representing one or more metrics each quantifying, prior
to the measurement system analysis, an estimate of contribution to
variation in evaluating the industrial product set according to the
measurement system analysis. The computer-program product includes
instructions to cause a computing system to, prior to the
measurement system analysis, generate an input design comprising a
respective input set for each respective measurement test of the
measurement system analysis. The respective input set comprises one
or more settings for conducting the respective measurement test of
the measurement system analysis. The input design is designed to
isolate candidate sources for contributing to the variation in
evaluating the industrial product set according to the measurement
system analysis. The computer-program product includes instructions
to cause a computing system to, prior to a measurement system
analysis, output, based on the metric set and the input design, the
one or more computer-generated likelihoods for the respective
candidate evaluations of the industrial product set according to
the measurement system analysis.
[0004] In another example embodiment, a computing device is
provided. The computing device includes, but is not limited to, a
processor and memory. The memory contains instructions that when
executed by the processor control the computing device to, prior to
the measurement system analysis, output, based on the metric set
and the input design, the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set according to the measurement system
analysis.
[0005] In another example embodiment, a method is provided of prior
to the measurement system analysis, outputting, based on the metric
set and the input design, the one or more computer-generated
likelihoods for the respective candidate evaluations of the
industrial product set according to the measurement system
analysis.
[0006] Other features and aspects of example embodiments are
presented below in the Detailed Description when read in connection
with the drawings presented with this application.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 illustrates a block diagram that provides an
illustration of the hardware components of a computing system,
according to at least one embodiment of the present technology.
[0008] FIG. 2 illustrates an example network including an example
set of devices communicating with each other over an exchange
system and via a network, according to at least one embodiment of
the present technology.
[0009] FIG. 3 illustrates a representation of a conceptual model of
a communications protocol system, according to at least one
embodiment of the present technology.
[0010] FIG. 4 illustrates a communications grid computing system
including a variety of control and worker nodes, according to at
least one embodiment of the present technology.
[0011] FIG. 5 illustrates a flow chart showing an example process
for adjusting a communications grid or a work project in a
communications grid after a failure of a node, according to at
least one embodiment of the present technology.
[0012] FIG. 6 illustrates a portion of a communications grid
computing system including a control node and a worker node,
according to at least one embodiment of the present technology.
[0013] FIG. 7 illustrates a flow chart showing an example process
for executing a data analysis or processing project, according to
at least one embodiment of the present technology.
[0014] FIG. 8 illustrates a block diagram including components of
an Event Stream Processing Engine (ESPE), according to at least one
embodiment of the present technology.
[0015] FIG. 9 illustrates a flow chart showing an example process
including operations performed by an event stream processing
engine, according to at least one embodiment of the present
technology.
[0016] FIG. 10 illustrates an ESP system interfacing between a
publishing device and multiple event subscribing devices, according
to at least one embodiment of the present technology.
[0017] FIG. 11 illustrates a flow chart of an example of a process
for generating and using a machine-learning model according to at
least one embodiment of the present technology.
[0018] FIG. 12 illustrates an example of a machine-learning model
as a neural network according to at least one embodiment of the
present technology.
[0019] FIG. 13 illustrates a block diagram of a system for
outputting one or more computer-generated likelihoods for candidate
evaluations of an industrial product set according to at least one
embodiment of the present technology.
[0020] FIG. 14 illustrates a flow diagram for outputting one or
more computer-generated likelihoods for candidate evaluations of an
industrial product set according to at least one embodiment of the
present technology.
[0021] FIGS. 15A-15D illustrate example graphical user interfaces
for generating likelihoods for candidate evaluations of an
industrial product set according to at least one embodiment of the
present technology.
[0022] FIG. 15E illustrates an example graphical user interface for
simulating a measurement system analysis according to at least one
embodiment of the present technology.
[0023] FIGS. 16A-16D illustrate an example comparison of
computer-generated likelihoods for candidate evaluations according
to a measurement system analysis to an actual evaluation of a
measurement system analysis according to at least one embodiment of
the present technology.
[0024] FIG. 16E illustrates an example graphical user interface for
computing, accounting for an assumed variance for an interaction, a
computer-generated likelihood for a candidate evaluation according
to at least one embodiment of the present technology.
[0025] FIGS. 17A-17C illustrate example graphical user interfaces
for changing variance estimates for updating computer-generated
likelihoods of respective candidate evaluations according to at
least one embodiment of the present technology.
[0026] FIGS. 18A-18D illustrate example graphical user interfaces
for user selection of an order of measurement tests in a
measurement system analysis according to at least one embodiment of
the present technology.
[0027] FIG. 19 illustrates an example graphical user interface for
analyzing and outputting a design for a measurement system analysis
according to at least one embodiment of the present technology.
DETAILED DESCRIPTION
[0028] In the following description, for the purposes of
explanation, specific details are set forth to provide a thorough
understanding of embodiments of the technology. However, it will be
apparent that various embodiments may be practiced without these
specific details. The figures and description are not intended to
be restrictive.
[0029] The ensuing description provides example embodiments only,
and is not intended to limit the scope, applicability, or
configuration of the disclosure. Rather, the ensuing description of
the example embodiments will provide those skilled in the art with
an enabling description for implementing an example embodiment. It
should be understood that various changes may be made in the
function and arrangement of elements without departing from the
spirit and scope of the technology as set forth in the appended
claims.
[0030] Specific details are given in the following description to
provide a thorough understanding of the embodiments. However, it
will be understood by one of ordinary skill in the art that the
embodiments may be practiced without these specific details. For
example, circuits, systems, networks, processes, and other
components may be shown as components in block diagram form in
order not to obscure the embodiments in unnecessary detail. In
other instances, well-known circuits, processes, algorithms,
structures, and techniques may be shown without unnecessary detail
in order to avoid obscuring the embodiments.
[0031] Also, it is noted that individual embodiments may be
described as a process which is depicted as a flowchart, a flow
diagram, a data flow diagram, a structure diagram, or a block
diagram. Although a flowchart may describe the operations as a
sequential process, many of the operations can be performed in
parallel or concurrently. In addition, the order of the operations
may be re-arranged. A process is terminated when its operations are
completed, but could have additional operations not included in a
figure. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process
corresponds to a function, its termination can correspond to a
return of the function to the calling function or the main
function.
[0032] Systems depicted in some of the figures may be provided in
various configurations. In some embodiments, the systems may be
configured as a distributed system where one or more components of
the system are distributed across one or more networks in a cloud
computing system.
[0033] FIG. 1 is a block diagram that provides an illustration of
the hardware components of a data transmission network 100,
according to embodiments of the present technology. Data
transmission network 100 is a specialized computer system that may
be used for processing large amounts of data where a large number
of computer processing cycles are required.
[0034] Data transmission network 100 may also include computing
environment 114. Computing environment 114 may be a specialized
computer or other machine that processes the data received within
the data transmission network 100. Data transmission network 100
also includes one or more network devices 102. Network devices 102
may include client devices that attempt to communicate with
computing environment 114. For example, network devices 102 may
send data to the computing environment 114 to be processed, may
send signals to the computing environment 114 to control different
aspects of the computing environment or the data it is processing,
among other reasons. Network devices 102 may interact with the
computing environment 114 through a number of ways, such as, for
example, over one or more networks 108. As shown in FIG. 1,
computing environment 114 may include one or more other systems.
For example, computing environment 114 may include a database
system 118 and/or a communications grid 120.
[0035] In other embodiments, network devices may provide a large
amount of data, either all at once or streaming over a period of
time (e.g., using event stream processing (ESP), described further
with respect to FIGS. 8-10), to the computing environment 114 via
networks 108. For example, network devices 102 may include network
computers, sensors, databases, or other devices that may transmit
or otherwise provide data to computing environment 114. For
example, network devices may include local area network devices,
such as routers, hubs, switches, or other computer networking
devices. These devices may provide a variety of stored or generated
data, such as network data or data specific to the network devices
themselves. Network devices may also include sensors that monitor
their environment or other devices to collect data regarding that
environment or those devices, and such network devices may provide
data they collect over time. Network devices may also include
devices within the internet of things, such as devices within a
home automation network. Some of these devices may be referred to
as edge devices, and may involve edge computing circuitry. Data may
be transmitted by network devices directly to computing environment
114 or to network-attached data stores, such as network-attached
data stores 110 for storage so that the data may be retrieved later
by the computing environment 114 or other portions of data
transmission network 100.
[0036] Data transmission network 100 may also include one or more
network-attached data stores 110. Network-attached data stores 110
are used to store data to be processed by the computing environment
114 as well as any intermediate or final data generated by the
computing system in non-volatile memory. However in certain
embodiments, the configuration of the computing environment 114
allows its operations to be performed such that intermediate and
final data results can be stored solely in volatile memory (e.g.,
RAM), without a requirement that intermediate or final data results
be stored to non-volatile types of memory (e.g., disk). This can be
useful in certain situations, such as when the computing
environment 114 receives ad hoc queries from a user and when
responses, which are generated by processing large amounts of data,
need to be generated on-the-fly. In this non-limiting situation,
the computing environment 114 may be configured to retain the
processed information within memory so that responses can be
generated for the user at different levels of detail as well as
allow a user to interactively query against this information.
[0037] Network-attached data stores may store a variety of
different types of data organized in a variety of different ways
and from a variety of different sources. For example,
network-attached data storage may include storage other than
primary storage located within computing environment 114 that is
directly accessible by processors located therein. Network-attached
data storage may include secondary, tertiary or auxiliary storage,
such as large hard drives, servers, virtual memory, among other
types. Storage devices may include portable or non-portable storage
devices, optical storage devices, and various other mediums capable
of storing, containing data. A machine-readable storage medium or
computer-readable storage medium may include a non-transitory
medium in which data can be stored and that does not include
carrier waves and/or transitory electronic signals. Examples of a
non-transitory medium may include, for example, a magnetic disk or
tape, optical storage media such as compact disk or digital
versatile disk, flash memory, memory or memory devices. A
computer-program product may include code and/or machine-executable
instructions that may represent a procedure, a function, a
subprogram, a program, a routine, a subroutine, a module, a
software package, a class, or any combination of instructions, data
structures, or program statements. A code segment may be coupled to
another code segment or a hardware circuit by passing and/or
receiving information, data, arguments, parameters, or memory
contents. Information, arguments, parameters, data, etc. may be
passed, forwarded, or transmitted via any suitable means including
memory sharing, message passing, token passing, network
transmission, among others. Furthermore, the data stores may hold a
variety of different types of data. For example, network-attached
data stores 110 may hold unstructured (e.g., raw) data, such as
manufacturing data (e.g., a database containing records identifying
products being manufactured with parameter data for each product,
such as colors and models) or product sales databases (e.g., a
database containing individual data records identifying details of
individual product sales).
[0038] The unstructured data may be presented to the computing
environment 114 in different forms such as a flat file or a
conglomerate of data records, and may have data values and
accompanying time stamps. The computing environment 114 may be used
to analyze the unstructured data in a variety of ways to determine
the best way to structure (e.g., hierarchically) that data, such
that the structured data is tailored to a type of further analysis
that a user wishes to perform on the data. For example, after being
processed, the unstructured time stamped data may be aggregated by
time (e.g., into daily time period units) to generate time series
data and/or structured hierarchically according to one or more
dimensions (e.g., parameters, attributes, and/or variables). For
example, data may be stored in a hierarchical data structure, such
as a ROLAP OR MOLAP database, or may be stored in another tabular
form, such as in a flat-hierarchy form.
[0039] Data transmission network 100 may also include one or more
server farms 106. Computing environment 114 may route select
communications or data to the one or more sever farms 106 or one or
more servers within the server farms. Server farms 106 can be
configured to provide information in a predetermined manner. For
example, server farms 106 may access data to transmit in response
to a communication. Server farms 106 may be separately housed from
each other device within data transmission network 100, such as
computing environment 114, and/or may be part of a device or
system.
[0040] Server farms 106 may host a variety of different types of
data processing as part of data transmission network 100. Server
farms 106 may receive a variety of different data from network
devices, from computing environment 114, from cloud network 116, or
from other sources. The data may have been obtained or collected
from one or more sensors, as inputs from a control database, or may
have been received as inputs from an external system or device.
Server farms 106 may assist in processing the data by turning raw
data into processed data based on one or more rules implemented by
the server farms. For example, sensor data may be analyzed to
determine changes in an environment over time or in real-time.
[0041] Data transmission network 100 may also include one or more
cloud networks 116. Cloud network 116 may include a cloud
infrastructure system that provides cloud services. In certain
embodiments, services provided by the cloud network 116 may include
a host of services that are made available to users of the cloud
infrastructure system on demand. Cloud network 116 is shown in FIG.
1 as being connected to computing environment 114 (and therefore
having computing environment 114 as its client or user), but cloud
network 116 may be connected to or utilized by any of the devices
in FIG. 1. Services provided by the cloud network can dynamically
scale to meet the needs of its users. The cloud network 116 may
include one or more computers, servers, and/or systems. In some
embodiments, the computers, servers, and/or systems that make up
the cloud network 116 are different from the user's own on-premises
computers, servers, and/or systems. For example, the cloud network
116 may host an application, and a user may, via a communication
network such as the Internet, on demand, order and use the
application.
[0042] While each device, server and system in FIG. 1 is shown as a
single device, it will be appreciated that multiple devices may
instead be used. For example, a set of network devices can be used
to transmit various communications from a single user, or remote
server 140 may include a server stack. As another example, data may
be processed as part of computing environment 114.
[0043] Each communication within data transmission network 100
(e.g., between client devices, between a device and connection
management system 150, between servers 106 and computing
environment 114 or between a server and a device) may occur over
one or more networks 108. Networks 108 may include one or more of a
variety of different types of networks, including a wireless
network, a wired network, or a combination of a wired and wireless
network. Examples of suitable networks include the Internet, a
personal area network, a local area network (LAN), a wide area
network (WAN), or a wireless local area network (WLAN). A wireless
network may include a wireless interface or combination of wireless
interfaces. As an example, a network in the one or more networks
108 may include a short-range communication channel, such as a
Bluetooth or a Bluetooth Low Energy channel. A wired network may
include a wired interface. The wired and/or wireless networks may
be implemented using routers, access points, bridges, gateways, or
the like, to connect devices in the network 114, as will be further
described with respect to FIG. 2. The one or more networks 108 can
be incorporated entirely within or can include an intranet, an
extranet, or a combination thereof. In one embodiment,
communications between two or more systems and/or devices can be
achieved by a secure communications protocol, such as secure
sockets layer (SSL) or transport layer security (TLS). In addition,
data and/or transactional details may be encrypted.
[0044] Some aspects may utilize the Internet of Things (IoT), where
things (e.g., machines, devices, phones, sensors) can be connected
to networks and the data from these things can be collected and
processed within the things and/or external to the things. For
example, the IoT can include sensors in many different devices, and
high value analytics can be applied to identify hidden
relationships and drive increased efficiencies. This can apply to
both big data analytics and real-time (e.g., ESP) analytics. IoT
may be implemented in various areas, such as for access
(technologies that get data and move it), embed-ability (devices
with embedded sensors), and services. Industries in the IoT space
may include automotive (connected car), manufacturing (connected
factory), smart cities, energy and retail. This will be described
further below with respect to FIG. 2.
[0045] As noted, computing environment 114 may include a
communications grid 120 and a transmission network database system
118. Communications grid 120 may be a grid-based computing system
for processing large amounts of data. The transmission network
database system 118 may be for managing, storing, and retrieving
large amounts of data that are distributed to and stored in the one
or more network-attached data stores 110 or other data stores that
reside at different locations within the transmission network
database system 118. The compute nodes in the grid-based computing
system 120 and the transmission network database system 118 may
share the same processor hardware, such as processors that are
located within computing environment 114.
[0046] FIG. 2 illustrates an example network including an example
set of devices communicating with each other over an exchange
system and via a network, according to embodiments of the present
technology. As noted, each communication within data transmission
network 100 may occur over one or more networks. System 200
includes a network device 204 configured to communicate with a
variety of types of client devices, for example client devices 230,
over a variety of types of communication channels.
[0047] As shown in FIG. 2, network device 204 can transmit a
communication over a network (e.g., a cellular network via a base
station 210). The communication can be routed to another network
device, such as network devices 205-209, via base station 210. The
communication can also be routed to computing environment 214 via
base station 210. For example, network device 204 may collect data
either from its surrounding environment or from other network
devices (such as network devices 205-209) and transmit that data to
computing environment 214.
[0048] Although network devices 204-209 are shown in FIG. 2 as a
mobile phone, laptop computer, tablet computer, temperature sensor,
motion sensor, and audio sensor respectively, the network devices
may be or include sensors that are sensitive to detecting aspects
of their environment. For example, the network devices may include
sensors such as water sensors, power sensors, electrical current
sensors, chemical sensors, optical sensors, pressure sensors,
geographic or position sensors (e.g., GPS), velocity sensors,
acceleration sensors, flow rate sensors, among others. Examples of
characteristics that may be sensed include force, torque, load,
strain, position, temperature, air pressure, fluid flow, chemical
properties, resistance, electromagnetic fields, radiation,
irradiance, proximity, acoustics, moisture, distance, speed,
vibrations, acceleration, electrical potential, electrical current,
among others. The sensors may be mounted to various components used
as part of a variety of different types of systems (e.g., an oil
drilling operation). The network devices may detect and record data
related to the environment that it monitors, and transmit that data
to computing environment 214.
[0049] As noted, one type of system that may include various
sensors that collect data to be processed and/or transmitted to a
computing environment according to certain embodiments includes an
oil drilling system. For example, the one or more drilling
operation sensors may include surface sensors that measure a hook
load, a fluid rate, a temperature and a density in and out of the
wellbore, a standpipe pressure, a surface torque, a rotation speed
of a drill pipe, a rate of penetration, a mechanical specific
energy, etc. and downhole sensors that measure a rotation speed of
a bit, fluid densities, downhole torque, downhole vibration (axial,
tangential, lateral), a weight applied at a drill bit, an annular
pressure, a differential pressure, an azimuth, an inclination, a
dog leg severity, a measured depth, a vertical depth, a downhole
temperature, etc. Besides the raw data collected directly by the
sensors, other data may include parameters either developed by the
sensors or assigned to the system by a client or other controlling
device. For example, one or more drilling operation control
parameters may control settings such as a mud motor speed to flow
ratio, a bit diameter, a predicted formation top, seismic data,
weather data, etc. Other data may be generated using physical
models such as an earth model, a weather model, a seismic model, a
bottom hole assembly model, a well plan model, an annular friction
model, etc. In addition to sensor and control settings, predicted
outputs, of for example, the rate of penetration, mechanical
specific energy, hook load, flow in fluid rate, flow out fluid
rate, pump pressure, surface torque, rotation speed of the drill
pipe, annular pressure, annular friction pressure, annular
temperature, equivalent circulating density, etc. may also be
stored in the data warehouse.
[0050] In another example, another type of system that may include
various sensors that collect data to be processed and/or
transmitted to a computing environment according to certain
embodiments includes a home automation or similar automated network
in a different environment, such as an office space, school, public
space, sports venue, or a variety of other locations. Network
devices in such an automated network may include network devices
that allow a user to access, control, and/or configure various home
appliances located within the user's home (e.g., a television,
radio, light, fan, humidifier, sensor, microwave, iron, and/or the
like), or outside of the user's home (e.g., exterior motion
sensors, exterior lighting, garage door openers, sprinkler systems,
or the like). For example, network device 102 may include a home
automation switch that may be coupled with a home appliance. In
another embodiment, a network device can allow a user to access,
control, and/or configure devices, such as office-related devices
(e.g., copy machine, printer, or fax machine), audio and/or video
related devices (e.g., a receiver, a speaker, a projector, a DVD
player, or a television), media-playback devices (e.g., a compact
disc player, a CD player, or the like), computing devices (e.g., a
home computer, a laptop computer, a tablet, a personal digital
assistant (PDA), a computing device, or a wearable device),
lighting devices (e.g., a lamp or recessed lighting), devices
associated with a security system, devices associated with an alarm
system, devices that can be operated in an automobile (e.g., radio
devices, navigation devices), and/or the like. Data may be
collected from such various sensors in raw form, or data may be
processed by the sensors to create parameters or other data either
developed by the sensors based on the raw data or assigned to the
system by a client or other controlling device.
[0051] In another example, another type of system that may include
various sensors that collect data to be processed and/or
transmitted to a computing environment according to certain
embodiments includes a power or energy grid. A variety of different
network devices may be included in an energy grid, such as various
devices within one or more power plants, energy farms (e.g., wind
farm, solar farm, among others) energy storage facilities,
factories, homes and businesses of consumers, among others. One or
more of such devices may include one or more sensors that detect
energy gain or loss, electrical input or output or loss, and a
variety of other efficiencies. These sensors may collect data to
inform users of how the energy grid, and individual devices within
the grid, may be functioning and how they may be made more
efficient.
[0052] Network device sensors may also perform processing on data
it collects before transmitting the data to the computing
environment 114, or before deciding whether to transmit data to the
computing environment 114. For example, network devices may
determine whether data collected meets certain rules, for example
by comparing data or values calculated from the data and comparing
that data to one or more thresholds. The network device may use
this data and/or comparisons to determine if the data should be
transmitted to the computing environment 214 for further use or
processing.
[0053] Computing environment 214 may include machines 220 and 240.
Although computing environment 214 is shown in FIG. 2 as having two
machines, 220 and 240, computing environment 214 may have only one
machine or may have more than two machines. The machines that make
up computing environment 214 may include specialized computers,
servers, or other machines that are configured to individually
and/or collectively process large amounts of data. The computing
environment 214 may also include storage devices that include one
or more databases of structured data, such as data organized in one
or more hierarchies, or unstructured data. The databases may
communicate with the processing devices within computing
environment 214 to distribute data to them. Since network devices
may transmit data to computing environment 214, that data may be
received by the computing environment 214 and subsequently stored
within those storage devices. Data used by computing environment
214 may also be stored in data stores 235, which may also be a part
of or connected to computing environment 214.
[0054] Computing environment 214 can communicate with various
devices via one or more routers 225 or other inter-network or
intra-network connection components. For example, computing
environment 214 may communicate with devices 230 via one or more
routers 225. Computing environment 214 may collect, analyze and/or
store data from or pertaining to communications, client device
operations, client rules, and/or user-associated actions stored at
one or more data stores 235. Such data may influence communication
routing to the devices within computing environment 214, how data
is stored or processed within computing environment 214, among
other actions.
[0055] Notably, various other devices can further be used to
influence communication routing and/or processing between devices
within computing environment 214 and with devices outside of
computing environment 214. For example, as shown in FIG. 2,
computing environment 214 may include a web server 240. Thus,
computing environment 214 can retrieve data of interest, such as
client information (e.g., product information, client rules, etc.),
technical product details, news, current or predicted weather, and
so on.
[0056] In addition to computing environment 214 collecting data
(e.g., as received from network devices, such as sensors, and
client devices or other sources) to be processed as part of a big
data analytics project, it may also receive data in real time as
part of a streaming analytics environment. As noted, data may be
collected using a variety of sources as communicated via different
kinds of networks or locally. Such data may be received on a
real-time streaming basis. For example, network devices may receive
data periodically from network device sensors as the sensors
continuously sense, monitor and track changes in their
environments. Devices within computing environment 214 may also
perform pre-analysis on data it receives to determine if the data
received should be processed as part of an ongoing project. The
data received and collected by computing environment 214, no matter
what the source or method or timing of receipt, may be processed
over a period of time for a client to determine results data based
on the client's needs and rules.
[0057] FIG. 3 illustrates a representation of a conceptual model of
a communications protocol system, according to embodiments of the
present technology. More specifically, FIG. 3 identifies operation
of a computing environment in an Open Systems Interaction model
that corresponds to various connection components. The model 300
shows, for example, how a computing environment, such as computing
environment 320 (or computing environment 214 in FIG. 2) may
communicate with other devices in its network, and control how
communications between the computing environment and other devices
are executed and under what conditions.
[0058] The model can include layers 302-314. The layers are
arranged in a stack. Each layer in the stack serves the layer one
level higher than it (except for the application layer, which is
the highest layer), and is served by the layer one level below it
(except for the physical layer, which is the lowest layer). The
physical layer is the lowest layer because it receives and
transmits raw bites of data and is the farthest layer from the user
in a communications system. On the other hand, the application
layer is the highest layer because it interacts directly with a
software application.
[0059] As noted, the model includes a physical layer 302. Physical
layer 302 represents physical communication and can define
parameters of that physical communication. For example, such
physical communication may come in the form of electrical, optical,
or electromagnetic signals. Physical layer 302 also defines
protocols that may control communications within a data
transmission network.
[0060] Link layer 304 defines links and mechanisms used to transmit
(i.e., move) data across a network. The link layer manages
node-to-node communications, such as within a grid computing
environment. Link layer 304 can detect and correct errors (e.g.,
transmission errors in the physical layer 302). Link layer 304 can
also include a media access control (MAC) layer and logical link
control (LLC) layer.
[0061] Network layer 306 defines the protocol for routing within a
network. In other words, the network layer coordinates transferring
data across nodes in a same network (e.g., such as a grid computing
environment). Network layer 306 can also define the processes used
to structure local addressing within the network.
[0062] Transport layer 308 can manage the transmission of data and
the quality of the transmission and/or receipt of that data.
Transport layer 308 can provide a protocol for transferring data,
such as, for example, a Transmission Control Protocol (TCP).
Transport layer 308 can assemble and disassemble data frames for
transmission. The transport layer can also detect transmission
errors occurring in the layers below it.
[0063] Session layer 310 can establish, maintain, and manage
communication connections between devices on a network. In other
words, the session layer controls the dialogues or nature of
communications between network devices on the network. The session
layer may also establish checkpointing, adjournment, termination,
and restart procedures.
[0064] Presentation layer 312 can provide translation for
communications between the application and network layers. In other
words, this layer may encrypt, decrypt and/or format data based on
data types known to be accepted by an application or network
layer.
[0065] Application layer 314 interacts directly with software
applications and end users, and manages communications between
them. Application layer 314 can identify destinations, local
resource states or availability and/or communication content or
formatting using the applications.
[0066] Intra-network connection components 322 and 324 are shown to
operate in lower levels, such as physical layer 302 and link layer
304, respectively. For example, a hub can operate in the physical
layer and a switch can operate in the link layer. Inter-network
connection components 326 and 328 are shown to operate on higher
levels, such as layers 306-314. For example, routers can operate in
the network layer and network devices can operate in the transport,
session, presentation, and application layers.
[0067] As noted, a computing environment 320 can interact with
and/or operate on, in various embodiments, one, more, all or any of
the various layers. For example, computing environment 320 can
interact with a hub (e.g., via the link layer) so as to adjust
which devices the hub communicates with. The physical layer may be
served by the link layer, so it may implement such data from the
link layer. For example, the computing environment 320 may control
which devices it will receive data from. For example, if the
computing environment 320 knows that a certain network device has
turned off, broken, or otherwise become unavailable or unreliable,
the computing environment 320 may instruct the hub to prevent any
data from being transmitted to the computing environment 320 from
that network device. Such a process may be beneficial to avoid
receiving data that is inaccurate or that has been influenced by an
uncontrolled environment. As another example, computing environment
320 can communicate with a bridge, switch, router or gateway and
influence which device within the system (e.g., system 200) the
component selects as a destination. In some embodiments, computing
environment 320 can interact with various layers by exchanging
communications with equipment operating on a particular layer by
routing or modifying existing communications. In another
embodiment, such as in a grid computing environment, a node may
determine how data within the environment should be routed (e.g.,
which node should receive certain data) based on certain parameters
or information provided by other layers within the model.
[0068] As noted, the computing environment 320 may be a part of a
communications grid environment, the communications of which may be
implemented as shown in the protocol of FIG. 3. For example,
referring back to FIG. 2, one or more of machines 220 and 240 may
be part of a communications grid computing environment. A gridded
computing environment may be employed in a distributed system with
non-interactive workloads where data resides in memory on the
machines, or compute nodes. In such an environment, analytic code,
instead of a database management system, controls the processing
performed by the nodes. Data is co-located by pre-distributing it
to the grid nodes, and the analytic code on each node loads the
local data into memory. Each node may be assigned a particular task
such as a portion of a processing project, or to organize or
control other nodes within the grid.
[0069] FIG. 4 illustrates a communications grid computing system
400 including a variety of control and worker nodes, according to
embodiments of the present technology. Communications grid
computing system 400 includes three control nodes and one or more
worker nodes. Communications grid computing system 400 includes
control nodes 402, 404, and 406. The control nodes are
communicatively connected via communication paths 451, 453, and
455. Therefore, the control nodes may transmit information (e.g.,
related to the communications grid or notifications), to and
receive information from each other. Although communications grid
computing system 400 is shown in FIG. 4 as including three control
nodes, the communications grid may include more or less than three
control nodes.
[0070] Communications grid computing system (or just
"communications grid") 400 also includes one or more worker nodes.
Shown in FIG. 4 are six worker nodes 410-420. Although FIG. 4 shows
six worker nodes, a communications grid according to embodiments of
the present technology may include more or less than six worker
nodes. The number of worker nodes included in a communications grid
may be dependent upon how large the project or data set is being
processed by the communications grid, the capacity of each worker
node, the time designated for the communications grid to complete
the project, among others. Each worker node within the
communications grid 400 may be connected (wired or wirelessly, and
directly or indirectly) to control nodes 402-406. Therefore, each
worker node may receive information from the control nodes (e.g.,
an instruction to perform work on a project) and may transmit
information to the control nodes (e.g., a result from work
performed on a project). Furthermore, worker nodes may communicate
with each other (either directly or indirectly). For example,
worker nodes may transmit data between each other related to a job
being performed or an individual task within a job being performed
by that worker node. However, in certain embodiments, worker nodes
may not, for example, be connected (communicatively or otherwise)
to certain other worker nodes. In an embodiment, worker nodes may
only be able to communicate with the control node that controls it,
and may not be able to communicate with other worker nodes in the
communications grid, whether they are other worker nodes controlled
by the control node that controls the worker node, or worker nodes
that are controlled by other control nodes in the communications
grid.
[0071] A control node may connect with an external device with
which the control node may communicate (e.g., a grid user, such as
a server or computer, may connect to a controller of the grid). For
example, a server or computer may connect to control nodes and may
transmit a project or job to the node. The project may include a
data set. The data set may be of any size. Once the control node
receives such a project including a large data set, the control
node may distribute the data set or projects related to the data
set to be performed by worker nodes. Alternatively, for a project
including a large data set, the data set may be receive or stored
by a machine other than a control node (e.g., a Hadoop data
node).
[0072] Control nodes may maintain knowledge of the status of the
nodes in the grid (i.e., grid status information), accept work
requests from clients, subdivide the work across worker nodes,
coordinate the worker nodes, among other responsibilities. Worker
nodes may accept work requests from a control node and provide the
control node with results of the work performed by the worker node.
A grid may be started from a single node (e.g., a machine,
computer, server, etc.). This first node may be assigned or may
start as the primary control node that will control any additional
nodes that enter the grid.
[0073] When a project is submitted for execution (e.g., by a client
or a controller of the grid) it may be assigned to a set of nodes.
After the nodes are assigned to a project, a data structure (i.e.,
a communicator) may be created. The communicator may be used by the
project for information to be shared between the project code
running on each node. A communication handle may be created on each
node. A handle, for example, is a reference to the communicator
that is valid within a single process on a single node, and the
handle may be used when requesting communications between
nodes.
[0074] A control node, such as control node 402, may be designated
as the primary control node. A server, computer or other external
device may connect to the primary control node. Once the control
node receives a project, the primary control node may distribute
portions of the project to its worker nodes for execution. For
example, when a project is initiated on communications grid 400,
primary control node 402 controls the work to be performed for the
project in order to complete the project as requested or
instructed. The primary control node may distribute work to the
worker nodes based on various factors, such as which subsets or
portions of projects may be completed most efficiently and in the
correct amount of time. For example, a worker node may perform
analysis on a portion of data that is already local (e.g., stored
on) the worker node. The primary control node also coordinates and
processes the results of the work performed by each worker node
after each worker node executes and completes its job. For example,
the primary control node may receive a result from one or more
worker nodes, and the control node may organize (e.g., collect and
assemble) the results received and compile them to produce a
complete result for the project received from the end user.
[0075] Any remaining control nodes, such as control nodes 404 and
406, may be assigned as backup control nodes for the project. In an
embodiment, backup control nodes may not control any portion of the
project. Instead, backup control nodes may serve as a backup for
the primary control node and take over as primary control node if
the primary control node were to fail. If a communications grid
were to include only a single control node, and the control node
were to fail (e.g., the control node is shut off or breaks) then
the communications grid as a whole may fail and any project or job
being run on the communications grid may fail and may not complete.
While the project may be run again, such a failure may cause a
delay (severe delay in some cases, such as overnight delay) in
completion of the project. Therefore, a grid with multiple control
nodes, including a backup control node, may be beneficial.
[0076] To add another node or machine to the grid, the primary
control node may open a pair of listening sockets, for example. A
socket may be used to accept work requests from clients, and the
second socket may be used to accept connections from other grid
nodes). The primary control node may be provided with a list of
other nodes (e.g., other machines, computers, servers) that will
participate in the grid, and the role that each node will fill in
the grid. Upon startup of the primary control node (e.g., the first
node on the grid), the primary control node may use a network
protocol to start the server process on every other node in the
grid. Command line parameters, for example, may inform each node of
one or more pieces of information, such as: the role that the node
will have in the grid, the host name of the primary control node,
the port number on which the primary control node is accepting
connections from peer nodes, among others. The information may also
be provided in a configuration file, transmitted over a secure
shell tunnel, recovered from a configuration server, among others.
While the other machines in the grid may not initially know about
the configuration of the grid, that information may also be sent to
each other node by the primary control node. Updates of the grid
information may also be subsequently sent to those nodes.
[0077] For any control node other than the primary control node
added to the grid, the control node may open three sockets. The
first socket may accept work requests from clients, the second
socket may accept connections from other grid members, and the
third socket may connect (e.g., permanently) to the primary control
node. When a control node (e.g., primary control node) receives a
connection from another control node, it first checks to see if the
peer node is in the list of configured nodes in the grid. If it is
not on the list, the control node may clear the connection. If it
is on the list, it may then attempt to authenticate the connection.
If authentication is successful, the authenticating node may
transmit information to its peer, such as the port number on which
a node is listening for connections, the host name of the node,
information about how to authenticate the node, among other
information. When a node, such as the new control node, receives
information about another active node, it will check to see if it
already has a connection to that other node. If it does not have a
connection to that node, it may then establish a connection to that
control node.
[0078] Any worker node added to the grid may establish a connection
to the primary control node and any other control nodes on the
grid. After establishing the connection, it may authenticate itself
to the grid (e.g., any control nodes, including both primary and
backup, or a server or user controlling the grid). After successful
authentication, the worker node may accept configuration
information from the control node.
[0079] When a node joins a communications grid (e.g., when the node
is powered on or connected to an existing node on the grid or
both), the node is assigned (e.g., by an operating system of the
grid) a universally unique identifier (UUID). This unique
identifier may help other nodes and external entities (devices,
users, etc.) to identify the node and distinguish it from other
nodes. When a node is connected to the grid, the node may share its
unique identifier with the other nodes in the grid. Since each node
may share its unique identifier, each node may know the unique
identifier of every other node on the grid. Unique identifiers may
also designate a hierarchy of each of the nodes (e.g., backup
control nodes) within the grid. For example, the unique identifiers
of each of the backup control nodes may be stored in a list of
backup control nodes to indicate an order in which the backup
control nodes will take over for a failed primary control node to
become a new primary control node. However, a hierarchy of nodes
may also be determined using methods other than using the unique
identifiers of the nodes. For example, the hierarchy may be
predetermined, or may be assigned based on other predetermined
factors.
[0080] The grid may add new machines at any time (e.g., initiated
from any control node). Upon adding a new node to the grid, the
control node may first add the new node to its table of grid nodes.
The control node may also then notify every other control node
about the new node. The nodes receiving the notification may
acknowledge that they have updated their configuration
information.
[0081] Primary control node 402 may, for example, transmit one or
more communications to backup control nodes 404 and 406 (and, for
example, to other control or worker nodes within the communications
grid). Such communications may sent periodically, at fixed time
intervals, between known fixed stages of the project's execution,
among other protocols. The communications transmitted by primary
control node 402 may be of varied types and may include a variety
of types of information. For example, primary control node 402 may
transmit snapshots (e.g., status information) of the communications
grid so that backup control node 404 always has a recent snapshot
of the communications grid. The snapshot or grid status may
include, for example, the structure of the grid (including, for
example, the worker nodes in the grid, unique identifiers of the
nodes, or their relationships with the primary control node) and
the status of a project (including, for example, the status of each
worker node's portion of the project). The snapshot may also
include analysis or results received from worker nodes in the
communications grid. The backup control nodes may receive and store
the backup data received from the primary control node. The backup
control nodes may transmit a request for such a snapshot (or other
information) from the primary control node, or the primary control
node may send such information periodically to the backup control
nodes.
[0082] As noted, the backup data may allow the backup control node
to take over as primary control node if the primary control node
fails without requiring the grid to start the project over from
scratch. If the primary control node fails, the backup control node
that will take over as primary control node may retrieve the most
recent version of the snapshot received from the primary control
node and use the snapshot to continue the project from the stage of
the project indicated by the backup data. This may prevent failure
of the project as a whole.
[0083] A backup control node may use various methods to determine
that the primary control node has failed. In one example of such a
method, the primary control node may transmit (e.g., periodically)
a communication to the backup control node that indicates that the
primary control node is working and has not failed, such as a
heartbeat communication. The backup control node may determine that
the primary control node has failed if the backup control node has
not received a heartbeat communication for a certain predetermined
period of time. Alternatively, a backup control node may also
receive a communication from the primary control node itself
(before it failed) or from a worker node that the primary control
node has failed, for example because the primary control node has
failed to communicate with the worker node.
[0084] Different methods may be performed to determine which backup
control node of a set of backup control nodes (e.g., backup control
nodes 404 and 406) will take over for failed primary control node
402 and become the new primary control node. For example, the new
primary control node may be chosen based on a ranking or
"hierarchy" of backup control nodes based on their unique
identifiers. In an alternative embodiment, a backup control node
may be assigned to be the new primary control node by another
device in the communications grid or from an external device (e.g.,
a system infrastructure or an end user, such as a server or
computer, controlling the communications grid). In another
alternative embodiment, the backup control node that takes over as
the new primary control node may be designated based on bandwidth
or other statistics about the communications grid.
[0085] A worker node within the communications grid may also fail.
If a worker node fails, work being performed by the failed worker
node may be redistributed amongst the operational worker nodes. In
an alternative embodiment, the primary control node may transmit a
communication to each of the operable worker nodes still on the
communications grid that each of the worker nodes should
purposefully fail also. After each of the worker nodes fail, they
may each retrieve their most recent saved checkpoint of their
status and re-start the project from that checkpoint to minimize
lost progress on the project being executed.
[0086] FIG. 5 illustrates a flow chart showing an example process
for adjusting a communications grid or a work project in a
communications grid after a failure of a node, according to
embodiments of the present technology. The process may include, for
example, receiving grid status information including a project
status of a portion of a project being executed by a node in the
communications grid, as described in operation 502. For example, a
control node (e.g., a backup control node connected to a primary
control node and a worker node on a communications grid) may
receive grid status information, where the grid status information
includes a project status of the primary control node or a project
status of the worker node. The project status of the primary
control node and the project status of the worker node may include
a status of one or more portions of a project being executed by the
primary and worker nodes in the communications grid. The process
may also include storing the grid status information, as described
in operation 504. For example, a control node (e.g., a backup
control node) may store the received grid status information
locally within the control node. Alternatively, the grid status
information may be sent to another device for storage where the
control node may have access to the information.
[0087] The process may also include receiving a failure
communication corresponding to a node in the communications grid in
operation 506. For example, a node may receive a failure
communication including an indication that the primary control node
has failed, prompting a backup control node to take over for the
primary control node. In an alternative embodiment, a node may
receive a failure that a worker node has failed, prompting a
control node to reassign the work being performed by the worker
node. The process may also include reassigning a node or a portion
of the project being executed by the failed node, as described in
operation 508. For example, a control node may designate the backup
control node as a new primary control node based on the failure
communication upon receiving the failure communication. If the
failed node is a worker node, a control node may identify a project
status of the failed worker node using the snapshot of the
communications grid, where the project status of the failed worker
node includes a status of a portion of the project being executed
by the failed worker node at the failure time.
[0088] The process may also include receiving updated grid status
information based on the reassignment, as described in operation
510, and transmitting a set of instructions based on the updated
grid status information to one or more nodes in the communications
grid, as described in operation 512. The updated grid status
information may include an updated project status of the primary
control node or an updated project status of the worker node. The
updated information may be transmitted to the other nodes in the
grid to update their stale stored information.
[0089] FIG. 6 illustrates a portion of a communications grid
computing system 600 including a control node and a worker node,
according to embodiments of the present technology. Communications
grid 600 computing system includes one control node (control node
602) and one worker node (worker node 610) for purposes of
illustration, but may include more worker and/or control nodes. The
control node 602 is communicatively connected to worker node 610
via communication path 650. Therefore, control node 602 may
transmit information (e.g., related to the communications grid or
notifications), to and receive information from worker node 610 via
path 650.
[0090] Similar to in FIG. 4, communications grid computing system
(or just "communications grid") 600 includes data processing nodes
(control node 602 and worker node 610). Nodes 602 and 610 include
multi-core data processors. Each node 602 and 610 includes a
grid-enabled software component (GESC) 620 that executes on the
data processor associated with that node and interfaces with buffer
memory 622 also associated with that node. Each node 602 and 610
includes a database management software (DBMS) 628 that executes on
a database server (not shown) at control node 602 and on a database
server (not shown) at worker node 610.
[0091] Each node also includes a data store 624. Data stores 624,
similar to network-attached data stores 110 in FIG. 1 and data
stores 235 in FIG. 2, are used to store data to be processed by the
nodes in the computing environment. Data stores 624 may also store
any intermediate or final data generated by the computing system
after being processed, for example in non-volatile memory. However
in certain embodiments, the configuration of the grid computing
environment allows its operations to be performed such that
intermediate and final data results can be stored solely in
volatile memory (e.g., RAM), without a requirement that
intermediate or final data results be stored to non-volatile types
of memory. Storing such data in volatile memory may be useful in
certain situations, such as when the grid receives queries (e.g.,
ad hoc) from a client and when responses, which are generated by
processing large amounts of data, need to be generated quickly or
on-the-fly. In such a situation, the grid may be configured to
retain the data within memory so that responses can be generated at
different levels of detail and so that a client may interactively
query against this information.
[0092] Each node also includes a user-defined function (UDF) 626.
The UDF provides a mechanism for the DMBS 628 to transfer data to
or receive data from the database stored in the data stores 624
that are managed by the DBMS. For example, UDF 626 can be invoked
by the DBMS to provide data to the GESC for processing. The UDF 626
may establish a socket connection (not shown) with the GESC to
transfer the data. Alternatively, the UDF 626 can transfer data to
the GESC by writing data to shared memory accessible by both the
UDF and the GESC.
[0093] The GESC 620 at the nodes 602 and 620 may be connected via a
network, such as network 108 shown in FIG. 1. Therefore, nodes 602
and 620 can communicate with each other via the network using a
predetermined communication protocol such as, for example, the
Message Passing Interface (MPI). Each GESC 620 can engage in
point-to-point communication with the GESC at another node or in
collective communication with multiple GESCs via the network. The
GESC 620 at each node may contain identical (or nearly identical)
software instructions. Each node may be capable of operating as
either a control node or a worker node. The GESC at the control
node 602 can communicate, over a communication path 652, with a
client device 630. More specifically, control node 602 may
communicate with client application 632 hosted by the client device
630 to receive queries and to respond to those queries after
processing large amounts of data.
[0094] DMBS 628 may control the creation, maintenance, and use of
database or data structure (not shown) within a nodes 602 or 610.
The database may organize data stored in data stores 624. The DMBS
628 at control node 602 may accept requests for data and transfer
the appropriate data for the request. With such a process,
collections of data may be distributed across multiple physical
locations. In this example, each node 602 and 610 stores a portion
of the total data managed by the management system in its
associated data store 624.
[0095] Furthermore, the DBMS may be responsible for protecting
against data loss using replication techniques. Replication
includes providing a backup copy of data stored on one node on one
or more other nodes. Therefore, if one node fails, the data from
the failed node can be recovered from a replicated copy residing at
another node. However, as described herein with respect to FIG. 4,
data or status information for each node in the communications grid
may also be shared with each node on the grid.
[0096] FIG. 7 illustrates a flow chart showing an example method
for executing a project within a grid computing system, according
to embodiments of the present technology. As described with respect
to FIG. 6, the GESC at the control node may transmit data with a
client device (e.g., client device 630) to receive queries for
executing a project and to respond to those queries after large
amounts of data have been processed. The query may be transmitted
to the control node, where the query may include a request for
executing a project, as described in operation 702. The query can
contain instructions on the type of data analysis to be performed
in the project and whether the project should be executed using the
grid-based computing environment, as shown in operation 704.
[0097] To initiate the project, the control node may determine if
the query requests use of the grid-based computing environment to
execute the project. If the determination is no, then the control
node initiates execution of the project in a solo environment
(e.g., at the control node), as described in operation 710. If the
determination is yes, the control node may initiate execution of
the project in the grid-based computing environment, as described
in operation 706. In such a situation, the request may include a
requested configuration of the grid. For example, the request may
include a number of control nodes and a number of worker nodes to
be used in the grid when executing the project. After the project
has been completed, the control node may transmit results of the
analysis yielded by the grid, as described in operation 708.
Whether the project is executed in a solo or grid-based
environment, the control node provides the results of the project
in operation 712.
[0098] As noted with respect to FIG. 2, the computing environments
described herein may collect data (e.g., as received from network
devices, such as sensors, such as network devices 204-209 in FIG.
2, and client devices or other sources) to be processed as part of
a data analytics project, and data may be received in real time as
part of a streaming analytics environment (e.g., ESP). Data may be
collected using a variety of sources as communicated via different
kinds of networks or locally, such as on a real-time streaming
basis. For example, network devices may receive data periodically
from network device sensors as the sensors continuously sense,
monitor and track changes in their environments. More specifically,
an increasing number of distributed applications develop or produce
continuously flowing data from distributed sources by applying
queries to the data before distributing the data to geographically
distributed recipients. An event stream processing engine (ESPE)
may continuously apply the queries to the data as it is received
and determines which entities should receive the data. Client or
other devices may also subscribe to the ESPE or other devices
processing ESP data so that they can receive data after processing,
based on for example the entities determined by the processing
engine. For example, client devices 230 in FIG. 2 may subscribe to
the ESPE in computing environment 214. In another example, event
subscription devices 1024a-c, described further with respect to
FIG. 10, may also subscribe to the ESPE. The ESPE may determine or
define how input data or event streams from network devices or
other publishers (e.g., network devices 204-209 in FIG. 2) are
transformed into meaningful output data to be consumed by
subscribers, such as for example client devices 230 in FIG. 2.
[0099] FIG. 8 illustrates a block diagram including components of
an Event Stream Processing Engine (ESPE), according to embodiments
of the present technology. ESPE 800 may include one or more
projects 802. A project may be described as a second-level
container in an engine model managed by ESPE 800 where a thread
pool size for the project may be defined by a user. Each project of
the one or more projects 802 may include one or more continuous
queries 804 that contain data flows, which are data transformations
of incoming event streams. The one or more continuous queries 804
may include one or more source windows 806 and one or more derived
windows 808.
[0100] The ESPE may receive streaming data over a period of time
related to certain events, such as events or other data sensed by
one or more network devices. The ESPE may perform operations
associated with processing data created by the one or more devices.
For example, the ESPE may receive data from the one or more network
devices 204-209 shown in FIG. 2. As noted, the network devices may
include sensors that sense different aspects of their environments,
and may collect data over time based on those sensed observations.
For example, the ESPE may be implemented within one or more of
machines 220 and 240 shown in FIG. 2. The ESPE may be implemented
within such a machine by an ESP application. An ESP application may
embed an ESPE with its own dedicated thread pool or pools into its
application space where the main application thread can do
application-specific work and the ESPE processes event streams at
least by creating an instance of a model into processing
objects.
[0101] The engine container is the top-level container in a model
that manages the resources of the one or more projects 802. In an
illustrative embodiment, for example, there may be only one ESPE
800 for each instance of the ESP application, and ESPE 800 may have
a unique engine name. Additionally, the one or more projects 802
may each have unique project names, and each query may have a
unique continuous query name and begin with a uniquely named source
window of the one or more source windows 806. ESPE 800 may or may
not be persistent.
[0102] Continuous query modeling involves defining directed graphs
of windows for event stream manipulation and transformation. A
window in the context of event stream manipulation and
transformation is a processing node in an event stream processing
model. A window in a continuous query can perform aggregations,
computations, pattern-matching, and other operations on data
flowing through the window. A continuous query may be described as
a directed graph of source, relational, pattern matching, and
procedural windows. The one or more source windows 806 and the one
or more derived windows 808 represent continuously executing
queries that generate updates to a query result set as new event
blocks stream through ESPE 800. A directed graph, for example, is a
set of nodes connected by edges, where the edges have a direction
associated with them.
[0103] An event object may be described as a packet of data
accessible as a collection of fields, with at least one of the
fields defined as a key or unique identifier (ID). The event object
may be created using a variety of formats including binary,
alphanumeric, XML, etc. Each event object may include one or more
fields designated as a primary identifier (ID) for the event so
ESPE 800 can support operation codes (opcodes) for events including
insert, update, upsert, and delete. Upsert opcodes update the event
if the key field already exists; otherwise, the event is inserted.
For illustration, an event object may be a packed binary
representation of a set of field values and include both metadata
and field data associated with an event. The metadata may include
an opcode indicating if the event represents an insert, update,
delete, or upsert, a set of flags indicating if the event is a
normal, partial-update, or a retention generated event from
retention policy management, and a set of microsecond timestamps
that can be used for latency measurements.
[0104] An event block object may be described as a grouping or
package of event objects. An event stream may be described as a
flow of event block objects. A continuous query of the one or more
continuous queries 804 transforms a source event stream made up of
streaming event block objects published into ESPE 800 into one or
more output event streams using the one or more source windows 806
and the one or more derived windows 808. A continuous query can
also be thought of as data flow modeling.
[0105] The one or more source windows 806 are at the top of the
directed graph and have no windows feeding into them. Event streams
are published into the one or more source windows 806, and from
there, the event streams may be directed to the next set of
connected windows as defined by the directed graph. The one or more
derived windows 808 are all instantiated windows that are not
source windows and that have other windows streaming events into
them. The one or more derived windows 808 may perform computations
or transformations on the incoming event streams. The one or more
derived windows 808 transform event streams based on the window
type (that is operators such as join, filter, compute, aggregate,
copy, pattern match, procedural, union, etc.) and window settings.
As event streams are published into ESPE 800, they are continuously
queried, and the resulting sets of derived windows in these queries
are continuously updated.
[0106] FIG. 9 illustrates a flow chart showing an example process
including operations performed by an event stream processing
engine, according to some embodiments of the present technology. As
noted, the ESPE 800 (or an associated ESP application) defines how
input event streams are transformed into meaningful output event
streams. More specifically, the ESP application may define how
input event streams from publishers (e.g., network devices
providing sensed data) are transformed into meaningful output event
streams consumed by subscribers (e.g., a data analytics project
being executed by a machine or set of machines).
[0107] Within the application, a user may interact with one or more
user interface windows presented to the user in a display under
control of the ESPE independently or through a browser application
in an order selectable by the user. For example, a user may execute
an ESP application, which causes presentation of a first user
interface window, which may include a plurality of menus and
selectors such as drop down menus, buttons, text boxes, hyperlinks,
etc. associated with the ESP application as understood by a person
of skill in the art. As further understood by a person of skill in
the art, various operations may be performed in parallel, for
example, using a plurality of threads.
[0108] At operation 900, an ESP application may define and start an
ESPE, thereby instantiating an ESPE at a device, such as machine
220 and/or 240. In an operation 902, the engine container is
created. For illustration, ESPE 800 may be instantiated using a
function call that specifies the engine container as a manager for
the model.
[0109] In an operation 904, the one or more continuous queries 804
are instantiated by ESPE 800 as a model. The one or more continuous
queries 804 may be instantiated with a dedicated thread pool or
pools that generate updates as new events stream through ESPE 800.
For illustration, the one or more continuous queries 804 may be
created to model business processing logic within ESPE 800, to
predict events within ESPE 800, to model a physical system within
ESPE 800, to predict the physical system state within ESPE 800,
etc. For example, as noted, ESPE 800 may be used to support sensor
data monitoring and management (e.g., sensing may include force,
torque, load, strain, position, temperature, air pressure, fluid
flow, chemical properties, resistance, electromagnetic fields,
radiation, irradiance, proximity, acoustics, moisture, distance,
speed, vibrations, acceleration, electrical potential, or
electrical current, etc.).
[0110] ESPE 800 may analyze and process events in motion or "event
streams." Instead of storing data and running queries against the
stored data, ESPE 800 may store queries and stream data through
them to allow continuous analysis of data as it is received. The
one or more source windows 806 and the one or more derived windows
808 may be created based on the relational, pattern matching, and
procedural algorithms that transform the input event streams into
the output event streams to model, simulate, score, test, predict,
etc. based on the continuous query model defined and application to
the streamed data.
[0111] In an operation 906, a publish/subscribe (pub/sub)
capability is initialized for ESPE 800. In an illustrative
embodiment, a pub/sub capability is initialized for each project of
the one or more projects 802. To initialize and enable pub/sub
capability for ESPE 800, a port number may be provided. Pub/sub
clients can use a host name of an ESP device running the ESPE and
the port number to establish pub/sub connections to ESPE 800.
[0112] FIG. 10 illustrates an ESP system 1000 interfacing between
publishing device 1022 and event subscribing devices 1024a-c,
according to embodiments of the present technology. ESP system 1000
may include ESP device or subsystem 1001, event publishing device
1022, an event subscribing device A 1024a, an event subscribing
device B 1024b, and an event subscribing device C 1024c. Input
event streams are output to ESP device 1001 by publishing device
1022. In alternative embodiments, the input event streams may be
created by a plurality of publishing devices. The plurality of
publishing devices further may publish event streams to other ESP
devices. The one or more continuous queries instantiated by ESPE
800 may analyze and process the input event streams to form output
event streams output to event subscribing device A 1024a, event
subscribing device B 1024b, and event subscribing device C 1024c.
ESP system 1000 may include a greater or a fewer number of event
subscribing devices of event subscribing devices.
[0113] Publish-subscribe is a message-oriented interaction paradigm
based on indirect addressing. Processed data recipients specify
their interest in receiving information from ESPE 800 by
subscribing to specific classes of events, while information
sources publish events to ESPE 800 without directly addressing the
receiving parties. ESPE 800 coordinates the interactions and
processes the data. In some cases, the data source receives
confirmation that the published information has been received by a
data recipient.
[0114] A publish/subscribe API may be described as a library that
enables an event publisher, such as publishing device 1022, to
publish event streams into ESPE 800 or an event subscriber, such as
event subscribing device A 1024a, event subscribing device B 1024b,
and event subscribing device C 1024c, to subscribe to event streams
from ESPE 800. For illustration, one or more publish/subscribe APIs
may be defined. Using the publish/subscribe API, an event
publishing application may publish event streams into a running
event stream processor project source window of ESPE 800, and the
event subscription application may subscribe to an event stream
processor project source window of ESPE 800.
[0115] The publish/subscribe API provides cross-platform
connectivity and endianness compatibility between ESP application
and other networked applications, such as event publishing
applications instantiated at publishing device 1022, and event
subscription applications instantiated at one or more of event
subscribing device A 1024a, event subscribing device B 1024b, and
event subscribing device C 1024c.
[0116] Referring back to FIG. 9, operation 906 initializes the
publish/subscribe capability of ESPE 800. In an operation 908, the
one or more projects 802 are started. The one or more started
projects may run in the background on an ESP device. In an
operation 910, an event block object is received from one or more
computing device of the event publishing device 1022.
[0117] ESP subsystem 1001 may include a publishing client 1002,
ESPE 800, a subscribing client A 1004, a subscribing client B 1006,
and a subscribing client C 1008. Publishing client 1002 may be
started by an event publishing application executing at publishing
device 1022 using the publish/subscribe API. Subscribing client A
1004 may be started by an event subscription application A,
executing at event subscribing device A 1024a using the
publish/subscribe API. Subscribing client B 1006 may be started by
an event subscription application B executing at event subscribing
device B 1024b using the publish/subscribe API. Subscribing client
C 1008 may be started by an event subscription application C
executing at event subscribing device C 1024c using the
publish/subscribe API.
[0118] An event block object containing one or more event objects
is injected into a source window of the one or more source windows
806 from an instance of an event publishing application on event
publishing device 1022. The event block object may be generated,
for example, by the event publishing application and may be
received by publishing client 1002. A unique ID may be maintained
as the event block object is passed between the one or more source
windows 806 and/or the one or more derived windows 808 of ESPE 800,
and to subscribing client A 1004, subscribing client B 1006, and
subscribing client C 1008 and to event subscription device A 1024a,
event subscription device B 1024b, and event subscription device C
1024c. Publishing client 1002 may further generate and include a
unique embedded transaction ID in the event block object as the
event block object is processed by a continuous query, as well as
the unique ID that publishing device 1022 assigned to the event
block object.
[0119] In an operation 912, the event block object is processed
through the one or more continuous queries 804. In an operation
914, the processed event block object is output to one or more
computing devices of the event subscribing devices 1024a-c. For
example, subscribing client A 1004, subscribing client B 1006, and
subscribing client C 1008 may send the received event block object
to event subscription device A 1024a, event subscription device B
1024b, and event subscription device C 1024c, respectively.
[0120] ESPE 800 maintains the event block containership aspect of
the received event blocks from when the event block is published
into a source window and works its way through the directed graph
defined by the one or more continuous queries 804 with the various
event translations before being output to subscribers. Subscribers
can correlate a group of subscribed events back to a group of
published events by comparing the unique ID of the event block
object that a publisher, such as publishing device 1022, attached
to the event block object with the event block ID received by the
subscriber.
[0121] In an operation 916, a determination is made concerning
whether or not processing is stopped. If processing is not stopped,
processing continues in operation 910 to continue receiving the one
or more event streams containing event block objects from the, for
example, one or more network devices. If processing is stopped,
processing continues in an operation 918. In operation 918, the
started projects are stopped. In operation 920, the ESPE is
shutdown.
[0122] As noted, in some embodiments, big data is processed for an
analytics project after the data is received and stored. In other
embodiments, distributed applications process continuously flowing
data in real-time from distributed sources by applying queries to
the data before distributing the data to geographically distributed
recipients. As noted, an event stream processing engine (ESPE) may
continuously apply the queries to the data as it is received and
determines which entities receive the processed data. This allows
for large amounts of data being received and/or collected in a
variety of environments to be processed and distributed in real
time. For example, as shown with respect to FIG. 2, data may be
collected from network devices that may include devices within the
internet of things, such as devices within a home automation
network. However, such data may be collected from a variety of
different resources in a variety of different environments. In any
such situation, embodiments of the present technology allow for
real-time processing of such data.
[0123] Aspects of the current disclosure provide technical
solutions to technical problems, such as computing problems that
arise when an ESP device fails which results in a complete service
interruption and potentially significant data loss. The data loss
can be catastrophic when the streamed data is supporting mission
critical operations such as those in support of an ongoing
manufacturing or drilling operation. An embodiment of an ESP system
achieves a rapid and seamless failover of ESPE running at the
plurality of ESP devices without service interruption or data loss,
thus significantly improving the reliability of an operational
system that relies on the live or real-time processing of the data
streams. The event publishing systems, the event subscribing
systems, and each ESPE not executing at a failed ESP device are not
aware of or effected by the failed ESP device. The ESP system may
include thousands of event publishing systems and event subscribing
systems. The ESP system keeps the failover logic and awareness
within the boundaries of out-messaging network connector and
out-messaging network device.
[0124] In one example embodiment, a system is provided to support a
failover when event stream processing (ESP) event blocks. The
system includes, but is not limited to, an out-messaging network
device and a computing device. The computing device includes, but
is not limited to, a processor and a computer-readable medium
operably coupled to the processor. The processor is configured to
execute an ESP engine (ESPE). The computer-readable medium has
instructions stored thereon that, when executed by the processor,
cause the computing device to support the failover. An event block
object is received from the ESPE that includes a unique identifier.
A first status of the computing device as active or standby is
determined. When the first status is active, a second status of the
computing device as newly active or not newly active is determined.
Newly active is determined when the computing device is switched
from a standby status to an active status. When the second status
is newly active, a last published event block object identifier
that uniquely identifies a last published event block object is
determined. A next event block object is selected from a
non-transitory computer-readable medium accessible by the computing
device. The next event block object has an event block object
identifier that is greater than the determined last published event
block object identifier. The selected next event block object is
published to an out-messaging network device. When the second
status of the computing device is not newly active, the received
event block object is published to the out-messaging network
device. When the first status of the computing device is standby,
the received event block object is stored in the non-transitory
computer-readable medium.
[0125] FIG. 11 is a flow chart of an example of a process for
generating and using a machine-learning model according to some
aspects. Machine learning is a branch of artificial intelligence
that relates to mathematical models that can learn from,
categorize, and make predictions about data. Such mathematical
models, which can be referred to as machine-learning models, can
classify input data among two or more classes; cluster input data
among two or more groups; predict a result based on input data;
identify patterns or trends in input data; identify a distribution
of input data in a space; or any combination of these. Examples of
machine-learning models can include (i) neural networks; (ii)
decision trees, such as classification trees and regression trees;
(iii) classifiers, such as Naive bias classifiers, logistic
regression classifiers, ridge regression classifiers, random forest
classifiers, least absolute shrinkage and selector (LASSO)
classifiers, and support vector machines; (iv) clusterers, such as
k-means clusterers, mean-shift clusterers, and spectral clusterers;
(v) factorizers, such as factorization machines, principal
component analyzers and kernel principal component analyzers; and
(vi) ensembles or other combinations of machine-learning models. In
some examples, neural networks can include deep neural networks,
feed-forward neural networks, recurrent neural networks,
convolutional neural networks, radial basis function (RBF) neural
networks, echo state neural networks, long short-term memory neural
networks, bi-directional recurrent neural networks, gated neural
networks, hierarchical recurrent neural networks, stochastic neural
networks, modular neural networks, spiking neural networks, dynamic
neural networks, cascading neural networks, neuro-fuzzy neural
networks, or any combination of these.
[0126] Different machine-learning models may be used
interchangeably to perform a task. Examples of tasks that can be
performed at least partially using machine-learning models include
various types of scoring; bioinformatics; cheminformatics; software
engineering; fraud detection; customer segmentation; generating
online recommendations; adaptive websites; determining customer
lifetime value; search engines; placing advertisements in real time
or near real time; classifying DNA sequences; affective computing;
performing natural language processing and understanding; object
recognition and computer vision; robotic locomotion; playing games;
optimization and metaheuristics; detecting network intrusions;
medical diagnosis and monitoring; or predicting when an asset, such
as a machine, will need maintenance.
[0127] Any number and combination of tools can be used to create
machine-learning models. Examples of tools for creating and
managing machine-learning models can include SAS.RTM. Enterprise
Miner, SAS.RTM. Rapid Predictive Modeler, and SAS.RTM. Model
Manager, SAS Cloud Analytic Services (CAS).RTM., SAS Viya.RTM. of
all which are by SAS Institute Inc. of Cary, N.C.
[0128] Machine-learning models can be constructed through an at
least partially automated (e.g., with little or no human
involvement) process called training. During training, input data
can be iteratively supplied to a machine-learning model to enable
the machine-learning model to identify patterns related to the
input data or to identify relationships between the input data and
output data. With training, the machine-learning model can be
transformed from an untrained state to a trained state. Input data
can be split into one or more training sets and one or more
validation sets, and the training process may be repeated multiple
times. The splitting may follow a k-fold cross-validation rule, a
leave-one-out-rule, a leave-p-out rule, or a holdout rule. An
overview of training and using a machine-learning model is
described below with respect to the flow chart of FIG. 11.
[0129] In block 1104, training data is received. In some examples,
the training data is received from a remote database or a local
database, constructed from various subsets of data, or input by a
user. The training data can be used in its raw form for training a
machine-learning model or pre-processed into another form, which
can then be used for training the machine-learning model. For
example, the raw form of the training data can be smoothed,
truncated, aggregated, clustered, or otherwise manipulated into
another form, which can then be used for training the
machine-learning model.
[0130] In block 1106, a machine-learning model is trained using the
training data. The machine-learning model can be trained in a
supervised, unsupervised, or semi-supervised manner. In supervised
training, each input in the training data is correlated to a
desired output. This desired output may be a scalar, a vector, or a
different type of data structure such as text or an image. This may
enable the machine-learning model to learn a mapping between the
inputs and desired outputs. In unsupervised training, the training
data includes inputs, but not desired outputs, so that the
machine-learning model has to find structure in the inputs on its
own. In semi-supervised training, only some of the inputs in the
training data are correlated to desired outputs.
[0131] In block 1108, the machine-learning model is evaluated. For
example, an evaluation dataset can be obtained, for example, via
user input or from a database. The evaluation dataset can include
inputs correlated to desired outputs. The inputs can be provided to
the machine-learning model and the outputs from the
machine-learning model can be compared to the desired outputs. If
the outputs from the machine-learning model closely correspond with
the desired outputs, the machine-learning model may have a high
degree of accuracy. For example, if 90% or more of the outputs from
the machine-learning model are the same as the desired outputs in
the evaluation dataset, the machine-learning model may have a high
degree of accuracy. Otherwise, the machine-learning model may have
a low degree of accuracy. The 90% number is an example only. A
realistic and desirable accuracy percentage is dependent on the
problem and the data.
[0132] In some examples, if the machine-learning model has an
inadequate degree of accuracy for a particular task, the process
can return to block 1106, where the machine-learning model can be
further trained using additional training data or otherwise
modified to improve accuracy. If the machine-learning model has an
adequate degree of accuracy for the particular task, the process
can continue to block 1110.
[0133] In block 1110, new data is received. In some examples, the
new data is received from a remote database or a local database,
constructed from various subsets of data, or input by a user. The
new data may be unknown to the machine-learning model. For example,
the machine-learning model may not have previously processed or
analyzed the new data.
[0134] In block 1112, the trained machine-learning model is used to
analyze the new data and provide a result. For example, the new
data can be provided as input to the trained machine-learning
model. The trained machine-learning model can analyze the new data
and provide a result that includes a classification of the new data
into a particular class, a clustering of the new data into a
particular group, a prediction based on the new data, or any
combination of these.
[0135] In block 1114, the result is post-processed. For example,
the result can be added to, multiplied with, or otherwise combined
with other data as part of a job. As another example, the result
can be transformed from a first format, such as a time series
format, into another format, such as a count series format. Any
number and combination of operations can be performed on the result
during post-processing.
[0136] A more specific example of a machine-learning model is the
neural network 1200 shown in FIG. 12. The neural network 1200 is
represented as multiple layers of interconnected neurons, such as
neuron 1208, that can exchange data between one another. The layers
include an input layer 1202 for receiving input data, a hidden
layer 1204, and an output layer 1206 for providing a result. The
hidden layer 1204 is referred to as hidden because it may not be
directly observable or have its input directly accessible during
the normal functioning of the neural network 1200. Although the
neural network 1200 is shown as having a specific number of layers
and neurons for exemplary purposes, the neural network 1200 can
have any number and combination of layers, and each layer can have
any number and combination of neurons.
[0137] The neurons and connections between the neurons can have
numeric weights, which can be tuned during training. For example,
training data can be provided to the input layer 1202 of the neural
network 1200, and the neural network 1200 can use the training data
to tune one or more numeric weights of the neural network 1200. In
some examples, the neural network 1200 can be trained using
backpropagation. Backpropagation can include determining a gradient
of a particular numeric weight based on a difference between an
actual output of the neural network 1200 and a desired output of
the neural network 1200. Based on the gradient, one or more numeric
weights of the neural network 1200 can be updated to reduce the
difference, thereby increasing the accuracy of the neural network
1200. This process can be repeated multiple times to train the
neural network 1200. For example, this process can be repeated
hundreds or thousands of times to train the neural network
1200.
[0138] In some examples, the neural network 1200 is a feed-forward
neural network. In a feed-forward neural network, every neuron only
propagates an output value to a subsequent layer of the neural
network 1200. For example, data may only move one direction
(forward) from one neuron to the next neuron in a feed-forward
neural network.
[0139] In other examples, the neural network 1200 is a recurrent
neural network. A recurrent neural network can include one or more
feedback loops, allowing data to propagate in both forward and
backward through the neural network 1200. This can allow for
information to persist within the recurrent neural network. For
example, a recurrent neural network can determine an output based
at least partially on information that the recurrent neural network
has seen before, giving the recurrent neural network the ability to
use previous input to inform the output.
[0140] In some examples, the neural network 1200 operates by
receiving a vector of numbers from one layer; transforming the
vector of numbers into a new vector of numbers using a matrix of
numeric weights, a nonlinearity, or both; and providing the new
vector of numbers to a subsequent layer of the neural network 1200.
Each subsequent layer of the neural network 1200 can repeat this
process until the neural network 1200 outputs a final result at the
output layer 1206. For example, the neural network 1200 can receive
a vector of numbers as an input at the input layer 1202. The neural
network 1200 can multiply the vector of numbers by a matrix of
numeric weights to determine a weighted vector. The matrix of
numeric weights can be tuned during the training of the neural
network 1200. The neural network 1200 can transform the weighted
vector using a nonlinearity, such as a sigmoid tangent or the
hyperbolic tangent. In some examples, the nonlinearity can include
a rectified linear unit, which can be expressed using the following
equation:
y=max(x,0)
where y is the output and x is an input value from the weighted
vector. The transformed output can be supplied to a subsequent
layer, such as the hidden layer 1204, of the neural network 1200.
The subsequent layer of the neural network 1200 can receive the
transformed output, multiply the transformed output by a matrix of
numeric weights and a nonlinearity, and provide the result to yet
another layer of the neural network 1200. This process continues
until the neural network 1200 outputs a final result at the output
layer 1206.
[0141] Other examples of the present disclosure may include any
number and combination of machine-learning models having any number
and combination of characteristics. The machine-learning model(s)
can be trained in a supervised, semi-supervised, or unsupervised
manner, or any combination of these. The machine-learning model(s)
can be implemented using a single computing device or multiple
computing devices, such as the communications grid computing system
400 discussed above.
[0142] Implementing some examples of the present disclosure at
least in part by using machine-learning models can reduce the total
number of processing iterations, time, memory, electrical power, or
any combination of these consumed by a computing device when
analyzing data. For example, a neural network may more readily
identify patterns in data than other approaches. This may enable
the neural network to analyze the data using fewer processing
cycles and less memory than other approaches, while obtaining a
similar or greater level of accuracy.
[0143] Some machine-learning approaches may be more efficiently and
speedily executed and processed with machine-learning specific
processors (e.g., not a generic CPU). Such processors may also
provide an energy savings when compared to generic CPUs. For
example, some of these processors can include a graphical
processing unit (GPU), an application-specific integrated circuit
(ASIC), a field-programmable gate array (FPGA), an artificial
intelligence (AI) accelerator, a neural computing core, a neural
computing engine, a neural processing unit, a purpose-built chip
architecture for deep learning, and/or some other machine-learning
specific processor that implements a machine learning approach or
one or more neural networks using semiconductor (e.g., silicon
(Si), gallium arsenide (GaAs)) devices. Furthermore, these
processors may also be employed in heterogeneous computing
architectures with a number of and a variety of different types of
cores, engines, nodes, and/or layers to achieve various energy
efficiencies, processing speed improvements, data communication
speed improvements, and/or data efficiency targets and improvements
throughout various parts of the system when compared to a
homogeneous computing architecture that employs CPUs for general
purpose computing.
[0144] One or more embodiments are related to a measurement system
analysis. A measurement system analysis can be useful for
evaluating a measurement process. For instance, a measurement
system analysis can be used to ensure that data being collected on
measuring products is accurate and appropriate. This can ensure
products tested according to the measurement system are properly
being manufactured. For instance, measured products could include
industrial products such as chemicals, metals, ceramics, polymers,
composites, woods, or a combination of materials, etc. The
industrial product measured in the measurement system analysis may
be consumed, used to produce, or form a component of a manufactured
product in a final form. For instance, the industrial product could
include a blade part for a table saw. The measurement analysis
could involve analyzing a measurement procedure for the blade
(e.g., measuring the blade's circumference or thickness for the
manufactured table saw). As another example, the industrial product
could be a part that is a chip pattern for manufacturing a computer
chip. The measurement analysis could involve analyzing a
measurement procedure for the chip pattern used to produce the
computer chip (e.g., measuring to ensure the chip pattern is within
a design specification). As another example, the industrial product
could be a chemical for forming, or consumed in forming, a cleaner.
The measurement system analysis could involve analyzing a
measurement procedure for the chemical (e.g., for measuring its pH
or volume needed for the cleaner).
[0145] A measurement system can have a system of different
measurement components (such as gauges, fixtures, software, and
personnel) that enables the quantification, validation, or
assessment of characteristics of an industrial product. Analyzing a
complex system like a measurement system can be resource and time
intensive. For example, a test of a measurement system could
include testing different operators (e.g., people with different
skill levels and training for measuring a saw blade), testing
different measurement tools (e.g., gauges, fixtures, test
equipment, and/or calibration systems for measuring a chip
pattern), testing different sampling plans or measurement plans
(e.g., different approaches for selecting sampled saw blades), and
testing different environments (e.g., testing properties of a
chemical at different temperatures or humidity).
[0146] Embodiments herein enable a user to create a design for a
measurement system. A measurement system analysis can comprise
tests for evaluating, according to a measurement standard, an
industrial product set comprising one or more industrial products
(e.g., different chemical samples for measurement). A user can
perform diagnostic measures that will allow the user to evaluate
the properties of the design prior to experimentation (e.g.,
balance of tested conditions, variance proportions, monitoring
classification, and probability of specification limit failure).
Alternatively, or additionally, embodiments provide a
computer-generated approach for generating likelihoods for
candidate evaluations of an industrial product set according to a
measurement system design prior to conducting a measurement system
analysis. For instance, a computing system can indicate how likely
a measurement system analysis is to classifying according to a
particular classification system like an EMP categorization, a
Gauge R&R study, an Analysis of Variance (ANOVA) and an
average/range test. This computer-generated information can be
useful for a user to ensure that the measurement system analysis
would beneficially classify a measurement system before investing
time and resources into the testing. If the computing system
indicates the measurement system would not beneficially classify,
the user can adjust the measurement system (e.g., to limit
variations in certain system components). For instance, users can
interact with the computing system to provide different prior
estimates of variances for measurement system components to assess
the properties of a design across different scenarios.
[0147] FIG. 13 illustrates a system 1300 for outputting one or more
computer-generated likelihoods for candidate evaluations of an
industrial product set. System 1300 includes a computing device
1302.
[0148] In one or more embodiments, the computing device 1302
includes one or more input interfaces 1308 for receiving
measurement system analysis information 1350. For instance, the
measurement system analysis information 1350 may indicate factor
information 1352 of factors for consideration in the measurement
system analysis (e.g., environmental influences on the measurement
system analysis). One example would be an operator factor
indicating multiple operator characteristics in an operator set,
such as number of operators and different types of operators
performing measurements. Additionally, or alternatively, the factor
information 1352 could include a gauge factor indicating
measurement tools of a tool set (e.g., number of measurement tools,
different types, or calibrations of measurement tools).
Additionally, or alternatively, the factor information 1352 could
include a part factor indicating a number or type of industrial
product for analysis. For example, if the measurement system
analysis is for measuring a rotating saw, factors in the
measurement system analysis could include a part factor for a
measured blade in the rotating saw, an operator factor for the
operators measuring the saw blade, and a gauge factor for the
calibration of the ruler for measuring the saw blade.
Alternatively, or additionally, the measurement system analysis
information 1350 may be predefined (e.g., a default operator
factor, product factor, and gauge factor may be predefined).
[0149] A measurement system analysis can comprise one or more
measurement tests for evaluating, according to a measurement
standard, the industrial product set comprising one or more
industrial products. For example, different tests could have
different operators, different rulers, and/or different measured
parts (e.g., if the measurement parts are destroyed in testing or
to have different samples). An individual measurement test could
have a respective setting for each member of a factor set
comprising one or more factors of the measurement system analysis
(e.g., a particular operator, a particular ruler, and a particular
blade identification in a measurement test for a saw blade).
[0150] In one or more embodiments, the computing device 1302
includes one or more input interfaces 1308 for receiving (e.g., via
a graphical user interface) a request 1360 for one or more
computer-generated likelihoods for respective candidate
evaluations. The request 1360 can be, for example, a request for
one or more computer-generated likelihoods for respective candidate
evaluations of an industrial product set according to the
measurement system analysis. For instance, the request could be for
a likelihood of classification into one of multiple groups for the
industrial product set according to a measurement standard or a
likelihood of going beyond a threshold related to the measurement
standard. For instance, the request could be for likelihoods of
being classified into monitoring types for an EMP monitoring
classification. As another example, if the part could fail to work
or safely operate at a certain level (such as a size or material
hardness), the computer-generated likelihood could relate to a
probability that the part would be determined under or over a level
according to the measurement process (e.g., over a certain size or
under a material hardness). Additionally, or alternatively, the
request could be for a likelihood of a particular variance
estimate, or variance contribution to a total variation (i.e., an
estimated proportion or range of estimated proportions) in a
measurement system analysis. The request 1360 could be a request
for an evaluation or prediction related to the measurement system
analysis before conducting the measurement system analysis (e.g.,
an evaluation or prediction for one or more candidate outcomes for
a measurement system analysis or a measure of uncertainty for the
evaluation or prediction).
[0151] In one or more embodiments, the request 1360 indicates a
metric set 1362 for processing the request. For example, the
request 1360 could include a metric set representing one or more
metrics each quantifying, prior to the measurement system analysis,
an estimate of contribution to variation in evaluating the
industrial product set according to the measurement system
analysis. For instance, a user may have an assumed variance
indicating a user estimate for an environmental influence on the
measurement system analysis. As an example, the metric set could
include an operator variance 1364 assumed for an operator set
comprising one or more operators measuring, in the measurement
system analysis, at least one of the industrial product set.
Additionally, or alternatively, the metric set includes a gauge
variance 1366 assumed for a tool set comprising one or more
measurement tools for measuring, in the measurement system
analysis, at least one member of the industrial product set.
Additionally, or alternatively, the metric set includes a part
variance 1368 assumed for operation of the industrial product set
in the measurement system analysis.
[0152] The request 1360 can be received from another computing
system 1324 or can be input by a user of the system 1300 (e.g., a
keyboard 1322 or mouse 1320 for user entry of data). Additionally,
or alternatively, the input interface 1308 comprises an internal
interface (e.g., the computing device comprises a touch screen for
user entry of data or for employing stored defaults). A metric
indicated by the request could be supplied by the user. For
instance, the user may estimate that measurements of the
measurement system process will vary by a certain amount depending
on the time of day or location, so the user may specify an
environmental assumed variance for an environmental variance.
Additionally, or alternatively, a metric could be a default in the
computing system (e.g., there may be some estimated variation from
a measurement tool from another measurement system analysis).
Alternatively, or additionally, the computing system could indicate
that metrics and/or factors be authorized by a user such as
providing an alert if a user tries to delete a part factor or fails
to provide an assumed variance for the part factor.
[0153] In one or more embodiments, the computing device 1302
includes one or more output interfaces 1310 for outputting output
1370 related to a measurement system analysis. For example, the
output 1370 could be related to computer-generated likelihoods for
a likely classification 1372 or probability 1374 for a measurement
system analysis. Additionally, or alternatively, the output 1370
comprises an input design 1342 for controlling testing of a
measurement system analysis. The outputting based on received
information can occur before a measurement system analysis is
performed. This can enable a user to save resources initially
planned for the measurement system analysis if it is unlikely to
produce a user desired outcome for the measurement system analysis.
The generated diagnostics can be used to improve the measurement
system analysis. For instance, test planner users could save
resources or better allocate resources if they find that what they
had planned for is more than enough to get a sufficient outcome.
Additionally, or alternatively, test planner users could realize
they do not have enough resources to achieve their goals. The test
planner user could then request additional resources or at least be
aware of the limitations of the measurement system analysis going
in. If they have limited resources, it will allow the test planner
user to determine how best to allocate those resources to get the
most useful information out of the study.
[0154] Output interface 1310 may be an internal interface (e.g., to
display on a graphical user interface displayed by the computing
device 1302) or output to one or more output devices 1306 (e.g.,
display 1326 or printer 1328) of system 1300.
[0155] The system 1300 is configured to exchange information
between devices in the system (e.g., via wired and/or wireless
transmission). For example, a network (not shown) can connect one
or more devices of system 1300 to one or more other devices of
system 1300. Alternatively, or additionally, the system is
integrated into one device (e.g., a touch screen for entry and
display of information).
[0156] The computing device 1302 has a computer-readable medium
1312 and a processor 1314. Computer-readable medium 1312 is an
electronic holding place or storage for information so the
information can be accessed by processor 1314. Computer-readable
medium 1312 can include, but is not limited to, any type of
random-access memory (RAM), any type of read only memory (ROM), any
type of flash memory, etc. such as magnetic storage devices (e.g.,
hard disk, floppy disk, magnetic strips), optical disks (e.g.,
compact disc (CD), digital versatile disc (DVD)), smart cards,
flash memory devices, etc.
[0157] Processor 1314 executes instructions (e.g., stored at the
computer-readable medium 1312). The instructions can be carried out
by a special purpose computer, logic circuits, or hardware
circuits. In one or more embodiments, processor 1314 is implemented
in hardware and/or firmware. Processor 1314 executes an
instruction, meaning it performs or controls the operations called
for by that instruction. The term "execution" is the process of
running an application or the carrying out of the operation called
for by an instruction. The instructions can be written using one or
more programming language, scripting language, assembly language,
etc. Processor 1314 in one or more embodiments can retrieve a set
of instructions from a permanent memory device and copy the
instructions in an executable form to a temporary memory device
that is generally some form of RAM, for example. Processor 1314
operably couples with components of computing device 1302 (e.g.,
input interface 1308, output interface 1310 and computer-readable
medium 1312) to receive, to send, and to process information.
[0158] In one or more embodiments, computer-readable medium 1312
stores instructions for execution by processor 1314. In one or more
embodiments, one or more applications stored on computer-readable
medium 1312 are implemented in software (e.g., computer-readable
and/or computer-executable instructions) stored in
computer-readable medium 1312 and accessible by processor 1314 for
execution of the instructions. The one or more application can be
integrated with other analytic tools, data analytics software
application and/or software architecture such as that offered by
SAS Institute Inc. of Cary, N.C., USA. Merely for illustration, the
applications are implemented using or integrated with one or more
SAS software tools such as JMP.RTM., Base SAS, SAS.RTM. Enterprise
Miner.TM., SAS/STAT.RTM., SAS.RTM. High Performance Analytics
Server, SAS.RTM. Visual Data Mining and Machine Learning, SAS.RTM.
LASR.TM. SAS.RTM. In-Database Products, SAS.RTM. Scalable
Performance Data Engine, SAS.RTM. Cloud Analytic Services,
SAS/OR.RTM., SAS/ETS.RTM., SAS.RTM. Inventory Optimization,
SAS.RTM. Inventory Optimization Workbench, SAS.RTM. Visual
Analytics, SAS.RTM. Viya.TM., SAS In-Memory Statistics for
Hadoop.RTM., SAS.RTM. Forecast Server, and SAS/IML.RTM. all of
which are developed and provided by SAS Institute Inc. of Cary,
N.C., USA.
[0159] One or more applications stored on computer-readable medium
1312 can be implemented as a Web application. For example, an
application can be configured to receive hypertext transport
protocol (HTTP) responses and to send HTTP requests. The HTTP
responses may include web pages such as hypertext markup language
(HTML) documents and linked objects generated in response to the
HTTP requests. Each web page may be identified by a uniform
resource locator (URL) that includes the location or address of the
computing device that contains the resource to be accessed in
addition to the location of the resource on that computing device.
The type of file or resource depends on the Internet application
protocol such as the file transfer protocol, HTTP, H.323, etc. The
file accessed may be a simple text file, an image file, an audio
file, a video file, an executable, a common gateway interface
application, a Java applet, an extensible markup language (XML)
file, or any other type of file supported by HTTP.
[0160] For example, in one or more embodiments, the
computer-readable medium 1312 comprises instructions for a
measurement system analysis application 1340. The measurement
system analysis application 1340 can generate and output an input
design 1342 for the measurement system analysis. Additionally, or
alternatively, the measurement system analysis application can
output (e.g., based on the metric set 1362 and/or the input design
1342) one or more computer-generated likelihoods for the respective
candidate evaluations of the industrial product set according to
the measurement system analysis. For example, the input design 1342
can comprise a respective input set 1344 for each respective
measurement test of the measurement system analysis. The respective
input set 1344 comprises one or more settings for conducting the
respective measurement test of the measurement system analysis. The
input design 1342 is designed to isolate candidate sources for
contributing to the variation in evaluating the industrial product
set according to the measurement system analysis (e.g., the effect
of operators on the variation, the effect of gauges or parts on the
variation, and the effect of a particular combination of an
operator and gauge on the variation). The variation makes it
difficult to compute likelihoods of candidate evaluations for the
measurement system analysis. To determine the likelihood for a
candidate evaluation, the measurement system analysis application
1340 may run complex or numerous simulation scenarios to simulate
results for a simulation of the measurement system analysis
according to the input design 1342 with the assumptions of the
metric set 1362.
[0161] In one or more embodiments, fewer, different, and additional
components can be incorporated into computing device 1302. For
instance, in one or more embodiments, there are multiple input
devices or computing systems (e.g., one to input the request 1360
and another to input the measurement system analysis information
1350). In the same or different embodiments, there are multiple
output devices or computing systems (e.g., one to display the
output 1370 and one to print the output 1370).
[0162] As another example, the same interface supports both input
interface 1308 and output interface 1310. For example, a touch
screen provides a mechanism for user input and for presentation of
output to the user. Alternatively, the input interface 1308 has
more than one input interface that uses the same or different
interface technology. Alternatively, or additionally, the output
interface 1310 has more than one output interface that uses the
same or different interface technology.
[0163] In one or more embodiments, a computing system (e.g., the
system 1300 or computing device 1302) implements a method as
described herein (e.g., a method shown in FIG. 14) for outputting
one or more computer-generated likelihoods for candidate
evaluations of an industrial product set.
[0164] FIG. 14 illustrates a flow diagram for a method 1400 of
outputting one or more computer-generated likelihoods for candidate
evaluations of an industrial product set. The method 1400 can be
conducted, for example, prior to a measurement system analysis.
[0165] The method 1400 comprises an operation 1401 for receiving a
request for one or more computer-generated likelihoods for
respective candidate evaluations of an industrial product set
according to the measurement system analysis. The request can be
explicit or implicit. For instance, the user may request, via a
graphical user interface, to generate an input design for the
measurement system analysis, which triggers or provides further
options to the user for a generation of an assessment of that input
design (e.g., one or more computer-generated likelihoods for
candidate evaluations).
[0166] The measurement system analysis comprises measurement tests
(e.g., as defined by an input design) for evaluating, according to
a measurement standard, the industrial product set comprising one
or more industrial products. Each measurement test of the
measurement tests has a respective setting for each member of a
factor set comprising one or more factors of the measurement system
analysis. The request indicates a metric set representing one or
more metrics each quantifying, prior to the measurement system
analysis, an estimate of contribution to variation in evaluating
the industrial product set according to the measurement system
analysis. For instance, the user may enter into a graphical user
interface certain values for variances for certain factors, or
combination of factors, or accept default variances.
[0167] The method 1400 comprises an operation 1402 for generating
an input design comprising a respective input set for each
respective measurement test of the measurement system analysis. The
respective input set comprises one or more settings for conducting
the respective measurement test of the measurement system analysis.
The input design is designed to isolate candidate sources for
contributing to the variation in evaluating the industrial product
set according to the measurement system analysis. For example, if a
candidate source for contributing to the variation is an operator,
the input design will provide coverage for a balance of tests for
different operators with controlled inputs for other factors.
[0168] The method 1400 comprises an operation 1403 for outputting,
based on the metric set and the input design, the one or more
computer-generated likelihoods for the respective candidate
evaluations of the industrial product set according to the
measurement system analysis. For instance, different probabilities
for classifications or outcomes for the measurement system analysis
may be output.
[0169] The method 1400 comprises an operation 1404 for receiving a
user indication to change one or more metrics of the metrics set.
For instance, the output could be an output to a graphical user
interface with an initial output for the one or more
computer-generated likelihoods for candidate evaluations and the
metric set. The method could repeat operations to generate an
updated output (e.g., dynamically or ad hoc update the graphical
user interface to display an updated output for the one or more
computer-generated likelihoods for candidate evaluations that
accounts for the user indication). This way the user can determine
if variance of a particular factor should be changed to get a
desired outcome for the measurement system analysis. For instance,
the user may be able to input a lower variance for operators if the
user invests in changing the training process for operators or
select operators with a certain training to reduce overall variance
in the measurement system analysis.
[0170] Operations in method 1400 could be performed in a different
order than presented in this example. For instance, operation 1402
could be performed simultaneously or before operation 1401. More or
fewer operations could be performed. For instance, operation 1404
could be optionally performed in some embodiments.
[0171] FIGS. 15A-15D illustrate example graphical user interfaces
for generating likelihoods for candidate evaluations of an
industrial product set.
[0172] FIG. 15A shows an example graphical user interface 1500 with
features specific to measurement system analysis. In this example,
users can specify the role of a factor in factor portion 1502 of
graphical user interface 1500. The factors in this example have
specified roles with a particular interpretation in measurement
system analysis including an operator factor, part factor, and
gauge factor. Additionally, or alternatively, factors typical of
measurement system analysis can be prepopulated in the graphical
user interface for the user to add or remove factors as needed.
[0173] The computing system can receive a user indication to
include one or more factors in the measurement system analysis
(e.g., user acceptance of predefined factors or user specifying
factors). For instance, in this example, the computing system
receives a user indication to include multiple factors in the
measurement system analysis including an operator factor indicating
multiple operator characteristics in an operator set, a gauge
factor indicating multiple measurement tools of a tool set, and a
part factor in the measurement system analysis indicating multiple
industrial products of a same type in the industrial product set. A
user can add factors (e.g., environmental or blocking factors)
using add factor control 1508 or can remove factors using remove
control 1510 (e.g., if a part factor is not needed because a
different part is used for every test).
[0174] In one or more embodiments, users can specify or change
default initial variance estimates for factors in variance column
1506 (e.g., a default variance of "1"). The variance estimates can
be used to generate the initial design diagnostics. In this
example, the user can rename a particular factor and/or levels by
checking the Show Levels checkbox 1504 to reveal the current factor
names and levels. FIG. 15B shows graphical user interface 1500
updated to shows level options in factor portion 1502 when Show
Levels checkbox 1504 is checked. The user can also specify or
adjust level options. For example, 5 levels (L1-L5) may be used to
indicate five different operators will be conducting measurements
in the measurement system analysis. Three levels of part factor
(L1-L3) may indicate that there will be a sample size of 3 parts
that are to be measured. Three levels of gauge factor (L1-L3) may
indicate that there will be three different gauge or measurement
techniques employed in the measurement tests. The factor rows can
be interactive to change the quantity of levels or to name them to
specific criteria (e.g., unique identifiers for the parts,
operators, or gauges).
[0175] The design control 1512 can be used to generate a design for
the measurement system analysis (e.g., a design that provides an
indication of which of the levels to use in each measurement test
for the different factors). The computing system can receive a
request for one or more computer-generated likelihoods for
candidate evaluations responsive to the selection of the design
control 1512 or further control options. The request can indicate a
metric set. In this example, the metric set indicates multiple
metrics pertaining to the measurement system analysis including an
assumed variance for the operator set comprising operators
measuring at least one of the industrial product set (e.g., a
variance for an operator factor); an assumed variance for the tool
set comprising measurement tools for measuring at least one member
of the industrial product set (e.g., a variance for a gauge
factor); and an assumed variance for the industrial product set
(e.g., a variance for a part factor). Assumed or accounted for
variances for generating information prior to a measurement system
analysis may also be considered an estimate or prior user guess of
the variances that could be observed in a conducted measurement
system analysis.
[0176] The computing system generates the input design by
generating respective inputs associated with each of multiple
operator characteristics of the operator set in the measurement
system analysis (e.g., L1-L5 of operator factor), respective inputs
associated with each of multiple measurement tools in the
measurement system analysis (e.g., L1-L3 of gauge factor), and
respective inputs associated with each of multiple industrial
products of the industrial product set in the measurement system
analysis (e.g., L1-L3 of part factor).
[0177] FIG. 15C shows a graphical user interface 1540 displaying a
representation of the generated design in input design 1542 with
specified inputs for each of the factors of discrete measurement
tests (i.e., runs). In this case, the design is a full factorial
design with the factors replicated according to the number of
replicates specified (e.g., for different measurement studies in a
measurement system analysis). In this example the number of
replicates is specified in text box 1544 as "2" to indicate a total
of three studies for the measurement system analysis.
[0178] Replicates for different measurement studies can be useful,
for example, in situations where a part is repeated to look at
whether reuse of a part in measurement testing influences the test
outcomes. In this example, a part is repeated in the input design
1542 (e.g., runs 1-3 have a part L1).
[0179] Some measurement system analysis involves non-destructive
tests for the industrial product set such that a member of the
industrial product set is reused in testing according to the
measurement system analysis. The request for computer-generated
likelihoods for candidate evaluations of the industrial product set
indicates a quantity of members of the industrial product set for
testing. In this case, FIG. 15A specified 3 levels for a part
factor for a sample size of 3 members. The computing system
generated the input design 1542 in FIG. 15C for the measurement
system analysis by generating respective conditions for testing
each member of the industrial product set for multiple factors for
the non-destructive test (e.g., gauge and operator factors). The
input design 1542 comprises at least two different sets of
conditions for each member of the industrial product set.
Embodiments herein are applicable to other types of testing (e.g.,
where a part is destroyed). For instance, in a destructive test
case, the part factor may have only one level specified, may be
marked as nested within one or more other factors, and/or provide
for the user to indicate a destructive test case, so the user need
not specify a part factor (e.g., in implementations where a part
factor is provided as a default).
[0180] In one or more embodiments, the computing system can output
one or more computer-generated likelihoods for candidate
evaluations accounting for an assumed variance for the factors
(e.g., accounting for an assumed variance for a tool set, for an
industrial product set, and an operator set).
[0181] For instance, in this example, the user can select the
design diagnostics option 1550 to explore diagnostic measures that
will allow users to evaluate the properties of their designs prior
to experimentation. Further, the design diagnostics can be specific
to measurement system analysis. For instance, with measurement
system analysis design diagnostics there may be an emphasis on
identifying a level of contributing variation from factors on
response variation. For example, a simplified model for two factors
could be represented as
.sigma..sub.Total.sup.2=.sigma..sub.1.sup.2+.sigma..sub.2.sup.2+.sigma..-
sub.1.times.2.sup.2+.sigma..sub.Error.sup.2
where .sigma. is variance coming from factors or combinations of
factors.
[0182] In contrast, more classic design diagnostics may emphasize
identifying effects of factors on a response. A simplified model
for two factors could be represented as
Y=.beta..sub.0+.beta..sub.1X.sub.1+.beta..sub.2X.sub.2+.beta..sub.1X.sub-
.1X.sub.2+.epsilon.
where Y is the response, X.sub.1 and X.sub.2 are factors, .beta.
are used to weight factors in the model, and .epsilon. represents
error in the model.
[0183] FIG. 15D shows an example graphical user interface 1560 with
selected design diagnostics option 1550. In this example, the
computing system has received a request for the computer-generated
likelihoods for candidate evaluations where the request indicates a
metric set comprises multiple metrics shown in variance column
1572. The multiple metrics indicate a first assumed variance ("1"
for an operator factor) for an operator set comprising one or more
operators measuring, in the measurement system analysis, at least
one of the industrial product set. The multiple metrics indicate a
second assumed variance ("1" for a gauge factor) for a tool set
comprising one or more measurement tools for measuring, in the
measurement system analysis, at least one member of the industrial
product set. The multiple metrics indicate a third assumed variance
("1" for a part factor) for an operation of the industrial product
set in the measurement system analysis. The computing system
outputs to the graphical user interface 1560, one or more
computer-generated likelihoods for candidate evaluations accounting
for the first assumed variance, the second assumed variance, and
the third assumed variance.
[0184] In this example, the user is presented with four diagnostic
metric categories. These design diagnostics account for the
variance estimates. The estimate portion 1562 presents users with
the current assumed variances for each potential factor in the
measurement system analysis along with the lower and upper bounds
of a confidence interval (e.g., a 95% confidence interval), which
can be used to assess the level or measure of uncertainty users can
expect from the design.
[0185] This estimate portion 1562 is interactive in that the
variances guesses in variance column 1572 can be edited by the user
with corresponding confidence intervals. All other diagnostic
measures can be updated by the computing system accordingly.
[0186] A measurement system analysis can be conducted to identify
sources of variation for the product measurement outside of
variation in the product itself. For instance, with operator
variation, different individuals could have their own procedures
for measuring product specifications. As another example with gauge
variation different tools of the same type could have their own
calibrations affecting variation.
[0187] The proportion portion 1564 presents the estimated variance
proportions based on the current assumed variances along with
corresponding 95% confidence intervals, which can be used to assess
the level or measure of uncertainty users can expect on the
variance proportions from the design. Different confidence
intervals could be used (e.g., an 85% confidence interval).
Estimates of proportion of variance are visually explained by
factor, or combination of factors, based on current variances
guesses with corresponding confidence intervals.
[0188] By identifying these external sources to a user, the user
can selectively address sources for improvement of the measurement
procedure (e.g., through standardized training, calibration, and/or
other improvements to the measurement procedure). For instance, in
this example, proportion portion 1564 displays in the graphical
user interface 1560 estimate statistics for proportion of the
variation attributable to an operator set. In this example, the
operator factor has a variance proportion of 28.6% with 95%
confidence interval bounds of 16.2% to 46.7%. In other words, the
computing system can determine with 95% confidence the proportion
of variation in a measurement system analysis that will come from
the operator is between 16.2% and 46.7% of a total variation.
Proportion portion 1564 displays in the graphical user interface
1560 estimate statistics for proportion of the variation
attributable to a tool set (e.g., the gauge factor has a variance
proportion of 28.6% with 95% confidence interval bounds of 4.8% to
42.7%). Proportion portion 1564 displays in the graphical user
interface 1560 estimate statistics for proportion of the variation
attributable to the operation of the industrial product set (e.g.,
the part factor has a variance proportion of 28.6% with 95%
confidence interval bounds of 4.7% to 39.5%). These three sources
of variation are estimated to equally contribute to the variation.
However, the confidence interval information, may indicate that an
operator is more likely to be a prominent contributor to the
variation. In this case, a user may focus on minimizing variation
in aspects of the operator set to improve the measurement system
process and may then update the variance for the operator factor to
see how this would affect the estimates before running a
measurement system analysis. For instance, if the measurement
process is to measure the circumference of a cylindrical part, some
operators may be incorrectly calculating the circumference by using
a diameter instead of a radius. By correcting this operator error,
variance could be reduced by a factor of 4 or reduced from 1 to
0.25.
[0189] The EMP monitoring classification 1566 presents the
probability of the measurement system analysis falling into one of
four EMP monitoring classifications. These classifications indicate
information regarding how well the measurement process can identify
part-to-part variations. These classifications are described in
more detail with respect to other examples. Other or additional
classifications could be used in these example interfaces (e.g.,
classifications for control charts and Gauge R&R studies).
[0190] The specification portion 1568 presents the probability of
the response falling outside the user-specified specification
limits based on a standard normal distribution response with the
current assumed variances for each factor affecting the process
along with a 95% confidence interval, which can be used to assess
the level or measure of uncertainty around the probability.
[0191] The metrics in this example are derived based on simulations
computed by the computing system, which in turn are based on the
assumed variances the user inputs at the design specification
window (e.g., graphical user interface 1500 in FIG. 15A). The user
can adjust those estimates in the editable table boxes of variance
column 1572 to explore the properties of their design under
different settings. This will allow them to create a more robust
design for their needs.
[0192] Simulations of variance of estimates can be done based on
different approaches (e.g., an ANOVA approach). For instance, the
computing system can consider a three-factor measurement system
analysis with a gauge, operator, and part factor according to the
model:
.sigma..sub.Total.sup.2=.sigma..sub.Gauge.sup.2+.sigma..sub.Op.sup.2+.si-
gma..sub.Part.sup.2+.sigma..sub.Gauge.times.Op.sup.2+.sigma..sub.Gauge.tim-
es.Part.sup.2+.sigma..sub.Op.times.Part.sup.2+.sigma..sub.Error.sup.2
where .sigma. is variance coming from factors or combinations of
factors (e.g., gauge.times.Op).
[0193] Estimates of each variance can be calculated using mean
squares:
.sigma..sub.Gauge.sup.2=MS.sub.Gauge-MS.sub.Gauge.times.Op-MS.sub.Gauge.-
times.Part+MS.sub.Error
[0194] The mean squares have known distributions in a simulation.
Once the variances have been simulated, they can be used to compute
confidence intervals and variance proportions.
[0195] In this example, the simulations are computed based on
method of moments estimates for the variances, which involve linear
combinations of mean square terms. For example, for a design with
three factors, an example ANOVA table is shown in Table
TABLE-US-00001 TABLE 1 Number Sum of Degrees of Factor of Levels
Squares Freedom Mean Square Operator a SS_o a-1 MS_o = SS_o/(a-1)
Gauge b SS_g b-1 MS_g = SS_g/(b-1) Part c SS_p c-1 MS_p =
SS_p/(c-1) Operator*Gauge a*b SS_og (a-1)(b-1) MS_og = SS_og/df
Operator*Part a*c SS_op (a-1)(c-1) MS_op = SS_op/df Gauge*Part b*c
SS_gp (b-1)(c-1) MS_gp = SS_gp/df Operator* a*b*c SS_ogp (a-1)(b-1)
MS_ogp = Gauge*Part (c-1) SS_ogp/df Error SSE abc(r-1) MSE =
SSE/df
[0196] Each of the mean squares is a random quantity, which can be
represented using a Chi-squared distribution. The expected value
for each mean square is some combination of the true variances for
each factor. For example, the expected value for MS_o according to
Table 1 is
bc.sigma..sub.Op.sup.2+c.sigma..sub.Op*Ga+b.sigma..sub.Op*Pa.sup.2+.sigm-
a..sub.Op*Ga*Pa.sup.2+.sigma..sub.Error.sup.2
[0197] The method of moments estimate for the variance of an
operator factor could then be computed as
.sigma. ^ Op 2 = MS o - MS og - MS op + MS ogp bc ##EQU00001##
[0198] Since the mean squares can be simulated using a known
distribution, the computing system simulates values of these mean
squares, computes the variances from these simulations, and then
computes variance proportions and other metrics from the variances.
The confidence intervals are generated by taking the appropriate
percentiles from the simulations.
[0199] A user could be presented with a single measurement
assessment, e.g., a primary diagnostic measure in proportion
portion 1564 based on simulations and user-input to generate
uncertainty intervals around the variance estimates and variance
proportions. Additional metrics can be provide including EMP
("Evaluating the Measurement Process") categorization in EMP
monitoring classification 1566 and probability of the response
exceeding user-provided spec limits in specification portion 1568,
both of which are assessments in measurement system analysis.
Additionally, or alternatively, a single computer-generated
likelihood could be presented (e.g., a single EMP monitoring
classification like in this case second class for the most likely
EMP monitoring classification). Users can interact with the one or
more generated diagnostics by providing different prior estimates
of variances to assess the properties of their design across a wide
range of scenarios.
[0200] In one or more embodiments, a computing system evaluates the
one or more computer-generated likelihoods for the respective
candidate evaluations according to the measurement system analysis
by simulating results for the simulation of the measurement system
analysis and displaying in a graphical user interface one or more
statistics or graphs related to the simulation. For example, as
shown in FIG. 15D confidence intervals (CI) are provided for the
computer-generated likelihoods for variance evaluations and
specification limit failure probability evaluations (i.e., 95% CI
lower and upper bounds). These CIs can be generated based on
numerous or complex simulations (e.g., hundreds or thousands of
simulations). By selecting the simulations results option 1570, a
user can see a graph related to the simulation.
[0201] In this example, the graphical user interface 1560 can be
used to estimate the level of certainty about an outcome, which is
not known yet because the measurement system analysis has not been
performed. This can be accomplished through simulations, which show
individual predictions of the variance terms that may be indicated
by the measurement system analysis. Thousands of measurement
studies can be simulated by the computing system that reports back
to the graphical user interface to display to the user what can be
expected in terms of the variation in their responses. If the
simulations indicate a wide range of estimates (e.g., a range of
0.0248 to 3.8448 for gauge in estimate portion 1562 of FIG. 15D),
that could indicate to a user they may not have a lot of certainty
in their estimates. However, if the range of estimates is tighter
(e.g., a range of 0.3625 to 0.6551 for error in estimate portion
1562 of FIG. 15D), then the user can expect to be confident in
their results.
[0202] FIG. 15E illustrates an example graphical user interface
1580 for simulating a measurement system analysis. The simulation
results outline provides a boxplot of the simulation results of
different candidate variance estimate proportions from the
simulations. In this example, simulation results for different
variance estimate proportions are shown for each of the operator,
part, gauge, and error factors. A box (e.g., box 1582) is used to
show the range in results from the first quartile (or 25.sup.th
percentile) to the third quartile (or 75.sup.th percentile). A
vertical line (e.g., line 1590) goes through the box at a medium of
the results, whereas a diamond (e.g., diamond 1586) is used to show
the average of simulation results. Whiskers (e.g., whisker 1584)
are shown to represent the minimum and maximum of the interquartile
range, with outliers shown as dotted points (e.g., points 1588)
beyond the whiskers. A user could instead or additionally see
summary statistics or export the results to a data table of the
simulated values for further exploration.
[0203] FIGS. 15A-15E were merely examples. Interactive graphical
user interfaces are shown and described herein. Graphical user
interfaces could be displayed to a user separately as shown in
FIGS. 15A-15E or differently (e.g., on the same display). Different
models could have been used to generate output according to
simulations. For example, nested models could be used both in
design creation and diagnostic measures.
[0204] FIGS. 16A-16D illustrates an example comparison of
computer-generated likelihoods to an outcome of a measurement
system analysis. FIG. 16A shows the graphical user interface 1600
with computer-generated likelihoods for candidate evaluations
(e.g., candidate EMP monitoring classifications 1602). EMP
monitoring classifications indicate information regarding the
measurement system such as classes defined by Donald Wheeler to
classify how well the measurement process can identify the
part-to-part variation. For instance, EMP monitoring
classifications can indicate Intraclass Correlation values that
indicate a proportion of the total variation attributable to the
part. If there is very little measurement variation coming from the
measurement process, this value would be close to 1. The EMP
monitoring classes can indicate an amount of process signal
attenuation (decrease) and the chance of detecting a 3 standard
error shift within 10 subgroups, using Wheeler's test one or all
four tests. Table 2 indicates an example table for correlating a
monitoring class with various information regarding assessing a
measurement process. An EMP classification is used only as an
example. Different classifications could be used instead or
different criteria for those monitoring classifications.
TABLE-US-00002 TABLE 2 Interclass Reduction of Change of Monitoring
correlation Process Detecting .+-. 3 Class coefficient Signal
Standard Error Shift First Class 0.8-1.0 <10% >99% with Rule
1 Second Class 0.5-0.8 10%-30% >88% with Rule 1 Third Class
0.2-0.5 30%-55% >91% with Rules 1, 2, 3, and 4 Fourth Class
0.0-0.2 >55% Rapidly Vanishing
[0205] In this example, the computing system has received a request
for one or more computer-generated likelihoods for candidate
evaluations where the request indicates a metric set comprises
multiple metrics shown in variance estimates table 1604. In this
example, the multiple metrics indicate a first assumed variance
("1.5" for a part factor) for an operation of the industrial
product set in the measurement system analysis. The multiple
metrics indicate a second assumed variance ("1" for an operator
factor) for an operator set comprising one or more operators
measuring, in the measurement system analysis, at least one of the
industrial product set. The multiple metrics indicate a third
assumed variance for a blocking factor ("1" for an X3 factor) that
is external to a measurement procedure but could have an influence
on the measurement procedure, such as the location, day of the
week, or time of day of a measurement test. A factor error can be
used to account for variation not attributable to any source. In
this case a variance of "0.5" has been assigned for error. The
computing system can generate the input design for the measurement
system analysis by including inputs for one or more measurement
system analysis factors and blocking factors, and outputs the one
or more computer-generated likelihoods for candidate evaluations
accounting for an impact of the one or more measurement system
analysis factors and blocking factors.
[0206] In one or more embodiments, the computing system outputs the
one or more computer-generated likelihoods for candidate
evaluations by determining an interclass correlation indicating a
proportion of a total variation in operation of the industrial
product set that is attributable to a member of the industrial
product set, and generating, based on the interclass correlation,
an EMP classification. For instance, graphical user interface 1600
shows EMP monitoring classifications 1602 indicating that if the
measurement system analysis were conducted, that the measurement
system would most likely be classified as a third class and is
second most likely to be classified as a second-class monitoring
type.
[0207] Other computer-generated likelihoods for candidate
evaluations are displayed such as variance proportions in
proportion portion 1608 and likelihood of failure to meet
specification limits in specification portion 1606. For instance,
proportion portion 1608 can quantify how much greater a variance
proportion the part factor has to the total variation (37.5% with a
95% confidence interval of between 15.4% to 72.95%) given its
greater assumed variance.
[0208] If the likelihoods for candidate evaluations seem acceptable
to the user, the user can conduct the measurement system analysis.
FIG. 16B shows a graphical user interface 1620 of an input design
1610 for the factors specified in FIG. 16A. The output 1612 of the
measurement system tests according to the input design 1610 is also
shown.
[0209] FIG. 16C shows a graphical user interface 1640 of plots of a
measurement system analysis for the output 1612 with an average of
output 1612 plotted for each of the different test scenarios and a
range of the output 1612 plotted for each of the different test
scenarios.
[0210] FIG. 16D shows a graphical user interface 1660 of the EMP
results from the measurement system analysis. As shown in portion
1670 the system was classified as a third-class system (with bias)
or (with bias and interaction between factors). This matches the
estimated classification in FIG. 16A. Classification with bias
takes bias factors such as from the operator and instrument into
account when calculating an outcome for the EMP classification. It
has a potential to be classified as second class with no bias from
factors or interaction between factors. In this case bias is taken
into account when computing the classification prior to the
measurement system analysis.
[0211] In one or more embodiments, a computing system can receive
an indication to include or exclude bias of a respective one of one
or more factors in the measurement system analysis, and an
interaction of multiple factors in the measurement system analysis.
This indication could be indicated implicitly (e.g., by providing
user defined variance to indicate to include bias) and not
providing a predefined variance for a combination of factors such
as Part*Operator to indicate not to include interaction between
factors. One or more computer-generated likelihoods for candidate
evaluations can then account for this indication of bias or
interactions as indicated.
[0212] FIG. 16E illustrates an example graphical user interface
1680 for computing, accounting for an assumed variance for an
interaction, a computer-generated likelihood for a candidate
evaluation.
[0213] In this example, the user has entered a variance for the
interaction factor 1682 of a part factor and operator factor
("part*operator" has a variance of "0.25"). An interaction variance
may be estimated, for example, based on other measurement system
tests or industry knowledge.
[0214] A computing system receives a user indication to include
multiple factors in the measurement system analysis (e.g., part
factor, operator factor, part*operator factor) and the metric set
indicates an assumed variance for an interaction of one of more of
the multiple factors in the measurement system analysis. The input
design isolates the interaction of the one of more of the multiple
factors in the measurement system analysis.
[0215] Graphical user interface 1680 shows an updated display of
computer-generated likelihoods for candidate evaluations in view of
these updated assumed variances. For instance, EMP monitoring
classification 1684 indicates that the measurement system is now
most likely to be classified as a second-class monitoring type.
Variance portion 1686 indicates that most of the variance is coming
from the part factor and specification portion 1688 indicates that
there is a 0.0015 probability of a part failing the specification.
Embodiments herein provide an interactive graphical user interface
for users to test scenarios for different variances coming from
factors of the measurement system or interaction of factors in the
measurement system.
[0216] FIGS. 17A-17C illustrate example graphical user interfaces
for changing variance estimates for updating a computer-generated
likelihood for a candidate evaluation. In FIG. 17A, the graphical
user interface 1700 shows various computer-generated likelihoods
for input design 1710. The generated likelihoods account for
assumed variance of "1.5" for a part factor, "1" for an operator
variable, and "0.5" for an error factor as shown in estimates
portion 1702. A confidence interval can be provided to indicate a
confidence in the assumed or estimated variance. For instance, a
1.5 variance may be assumed for a part factor, but it is estimated
by the computing system with 95% confidence that variance estimated
to be observed in a measurement system analysis would fall between
0.477 to 3.2799 as displayed. A specification portion 1708 shows a
likelihood for the measured part failing to meet specification
limits (0.0044%) in specification portion 1708 of graphical user
interface. The EMP monitoring classification 1704 shows the
greatest likelihood of being classified as a second-class
monitoring type, and most of the variance comes from the part as
shown in proportion portion 1706 of graphical user interface
1700.
[0217] A user may test different scenarios to see how that would
impact the likely classification. In FIG. 17B, the user increases
the operator variance from "1" to "2" (e.g., accounting for less
supervision or training of operators). Graphical user interface
1730 shows the updated values accounting for this change in
variance. As shown in EMP monitoring classification 1734, this
change could result in a likely downgrade to a third-class
monitoring group. It would also increase the likelihood of the part
failing to meet specifications as shown in specification portion
1738. The variance proportion estimates show a greater variation
proportion coming from the operator now in proportion portion
1736.
[0218] In FIG. 17C, the user decreases the operator variance to
"0.5" (e.g., accounting for greater supervision or training of
operators). Graphical user interface 1760 shows the updated values
accounting for this change in variance. As shown in EMP monitoring
classification 1764, this change could result in a greater
likelihood of classification into the second class. It would also
decrease the likelihood of the part failing to meet specifications
as shown in specification portion 1738, and the part would likely
have the greatest influence on variation in proportion portion
1766. Accordingly, embodiments provide an interactive graphical
user interface that allows users to test out scenarios before
expending the cost and resources to conduct a measurement system
analysis.
[0219] Previous examples assumed complete randomization of run
order. One or more embodiments provide for restrictions in the
randomization of run order in measurement system analysis designs.
For instance, FIGS. 18A-18D illustrate example graphical user
interfaces for user selection of the order of measurement tests in
a measurement system analysis.
[0220] In one or more embodiments, replicates are controlled to
restrict randomization. Replicates can be used to provide multiple
measurement tests of similar conditions for different measurement
studies of a measurement system analysis. For instance, a computing
system can receive an indication of a restriction on generating an
input design that restricts an ordering for the measurement tests
in the measurement system analysis. The computing system can
generate the input design based on the restriction to accommodate
the ordering. For example, the graphical user interface 1800 in
FIG. 18A is an interactive graphical user interface that allows a
user to select a quantity of replicates in text box 1802 (e.g., 2
replicates). The graphical user interface 1800 also allows the user
to make selections regarding the treatment of those replicates in
measurement tests (i.e., runs). For instance, replicates control
1804 allows a user to select between a fast repeat, a batch repeat
or completely randomized run order. In this example, a default is
selected for a fast repeat run order. If the user selects design
control 1806, an input design is generated that ensures a fast
repeat of replicates in the run order.
[0221] The inputs for the factors in the different measurement
studies or replicates is set to randomize in the specification of
factors in table 1808. However, these factors can also be changed
to have different characteristics as well. For instance, a factor
could be fixed to be assigned only one type of level in a design.
This would also restrict randomization in the design.
[0222] FIG. 18B shows a graphical user interface 1820 displaying
the input design 1830. The graphical user interface 1820 indicates
in proximity the restrictions on the input design 1830 made in
graphical user interface 1800 (i.e., text box 1802 and replicates
control 1804). In this example, fast repeat is selected for the run
order. This is useful in situations in which for practical reasons
it may be inefficient to switch out inputs for a factor going from
one test to the next. It is useful in the design to have repeating
inputs for a particular factor (i.e., in a groups of measurement
tests).
[0223] In one or more embodiments, a user indication of a
restriction may indicate to restrict the ordering to indicate an
ordering for groups of the measurements tests for respective
members of a component of the measurement system analyzed by the
measurement system analysis (e.g., fast repeat run order). The
computing system generates the input design to indicate the groups
of the measurement tests for the respective members of the
component of the measurement system. For instance, operators may be
a component of a measurement system and they may do the tests in
shifts (e.g., the first set of tests are conducted by the first
operator and then the next operator comes in, etc.). It may be
helpful to have measurement tests grouped by operator. In the
example in FIG. 18B, parts are a component of a measurement system,
and all the parts of a particular type are grouped together. For
example, parts L3 are in a first group 1835 at the beginning of
part column 1834 of the input design 1830. This can be useful, if
for example, it is time consuming to switch out a part for each
test. Runs column 1832 indicates the order of the measurement tests
and operator column 1836 indicates the operator who will be testing
the part. Replicates column 1838 can be used to indicate a
measurement tests association with one of distinct studies
according to the first measurement study and additional two
replicates.
[0224] Table control 1824 can be used to generate a table of the
input design 1830 (e.g., to print out to operators performing the
measurement test according to the input design 1830).
[0225] If design diagnostic options 1822 are expanded, one or more
computer-generated likelihoods for the respective candidate
evaluations according to the measurement system analysis will
display in graphical user interface 1820 as shown in other examples
(e.g., an EMP classification likelihoods, variation proportion
estimates, and likelihood of failure to meet specification). These
generated likelihoods for candidate evaluations will be generated
based on the design and based on the restrictions for the design.
As with previous examples, the design diagnostic options 1822 can
be explored prior to performing the measurement system analysis
(e.g., to show computer-generated likelihoods for candidate
evaluations described herein).
[0226] FIG. 18C shows a graphical user interface 1840 for the
factors specified in FIG. 18A with a restriction in the replicates
control 1844 for "batch repeat" run order. In the example, in FIG.
18C, the measurement system analysis comprises multiple studies
with replicated test conditions in each of the multiple studies.
The request to generate computer-generated likelihoods for
candidate evaluations in this example indicates an amount of the
multiple studies of the industrial product set for the measurement
system analysis (e.g., a study and 2 replicates as indicated in
text box 1802). The batch repeat run order is a user indication of
a restriction to group measurement tests of a respective individual
study of the multiple studies in an ordering for measurement tests.
The computing system generates an input design to indicate groups
of the measurement tests grouped in the respective individual study
of the multiple studies. For instance, as shown in design 1850 of
graphical user interface 1840, the replicates column 1858 has a
grouping 1857 of measurement tests of a first measurement study
("1"). Similar test conditions can be found in each of the
measurement study groups. For instance, part column 1854 and
operator column 1856 specifies different part and operators for
measurements tests identified in run column 1852. Test conditions
1851 for measurement study 1 match test conditions 1853 found in
measurement study 2.
[0227] If design diagnostic options 1842 are expanded, one or more
computer-generated likelihoods for the respective candidate
evaluations according to the measurement system analysis will
display in graphical user interface 1840 as shown in other examples
(e.g., an EMP classification, variation proportion estimates, and
likelihood of failure to meet specification). These generated
likelihoods for candidate evaluations will be generated based on
the design and based on the restrictions for the design. As with
previous examples, the design diagnostic options 1842 can be
explored prior to performing the measurement system analysis (e.g.,
to show computer-generated likelihoods for candidate evaluations
described herein).
[0228] FIG. 18D shows a graphical user interface 1860 for the
factors specified in FIG. 18A with a user specification for
ordering in the replicates control 1864 for "completely randomized"
run order. As shown in design 1870 of graphical user interface
1860, settings are varied for conditions in the parts column 1874,
the operator column 1876 and the replicates column 1878 for the
measurement tests identified in runs column 1872. This
randomization contrasts with the similarities described in FIGS.
18B and 18C due to restrictions on assignment.
[0229] If design diagnostic options 1862 are expanded, one or more
computer-generated likelihoods for the respective candidate
evaluations according to the measurement system analysis will
display in graphical user interface 1860 as shown in other examples
(e.g., an EMP classification, variation proportion estimates,
likelihood of failure to meet specification). These generated
likelihoods for candidate evaluations will be generated based on
the design and based on the restrictions for the design. As with
previous examples, the design diagnostic options 1862 can be
explored prior to performing the measurement system analysis (e.g.,
to show computer-generated likelihoods for candidate evaluations
described herein).
[0230] In these examples in FIGS. 18A-18D, the measurement system
analysis was for a non-destructive test for the industrial product
set such that a member of the industrial product set is reused in
testing according to the measurement system analysis. In this case,
FIG. 18A specified 3 levels for a part factor for a sample size of
3 members. The computing system generated the input designs in
FIGS. 18B-18D for these 3 members.
[0231] In some embodiments, the measurement system analysis is for
a destructive test for an industrial product set such that a member
of the industrial product set is destroyed in testing according to
the measurement system analysis. For example, if an alkaline
material with a certain ph tolerance is needed for forming a
cleaner, the measurement test for measuring the ph of the alkaline
material may alter the materials (e.g., change the color).
Different samples may be needed for each measurement test. In this
case a part factor may not be specified by a user or may be
specified with the same number of levels as runs. A measurement
system analysis involving a measurement procedure with a color
variation may be very difficult to implement without variation
(e.g., operators' eyesight are not identical, color gauges are not
necessarily printed consistently, time of day may be a blocking
factor impacting how the operator sees the color in the test). An
input design for a measurement system analysis to consider an
operator factor, a gauge factor, and a blocking factor may be
needed.
[0232] The computing system can generate an input design for the
measurement system analysis by generating respective conditions for
testing each member of the industrial product set for multiple
factors for the destructive test. For instance, FIG. 19 illustrates
an example graphical user interface 1900 for analyzing and
outputting a design for a measurement system analysis. FIG. 19
shows an input design 1902 in the graphical user interface 1900. In
this example, there are three factors blocking factor X12,
operator, and gauge that can each take one of two levels (1 or -1).
There is no part factor specified since each measurement test or
run will have a different member of a same part type in the
measurement system analysis. The input design indicates the
respective conditions for other parts of the measurement system
analysis. This is just one example implementation for
computer-generated designs for destructive testing designs. There
could be additional or alternative implementations such as an
additional column uniquely identifying each part and/or an
additional indicator for destructive testing.
[0233] Embodiments herein can be integrated with other design
diagnostic tools for designs generated according to embodiments
herein. For instance, one or more embodiments may be integrated
into a Design of Experiments (DOE) platform in JMP.RTM. provided by
SAS Institute Inc. in Cary, N.C. This can advantageously allow
users to create designs specifically for the purpose of conducting
a measurement system analysis and provide for design diagnostics
specific to measurement system analysis goals and/or generally
applicable to different design types.
[0234] For instance, the input design 1902 can be analyzed in a
custom design tool. A power analysis 1904 can be used to study the
probability of detecting a significant effect in a factor. For
instance, there can be a fixed factor in the measurement system
analysis which is not taken as a random subset of a population. For
fixed factors, the bias due to each factor level is important and
can be evaluated using the power analysis 1904. A prediction
variance profile 1906 can be used to compute a relative prediction
variance that depends on the design and factor settings and can be
calculated before running the measurement system analysis. A
smaller prediction variance is associated with a better design. A
fraction of design space plot 1908 can be used to show the
proportion of the design space over which the relative prediction
variance lies where better designs have a larger proportion of the
design space with low prediction variance values. A prediction
variance surface 1910 shows a plot of the relative prediction
variances for variables. An estimation efficiency 1912 can be used
to give the fractional increase in confidence interval length and
relative standard error of estimate for each parameter estimate in
a model of the measurement system analysis. An alias matrix 1914
and color map on correlations 1916 can be used to evaluate
relationships between factors of the measurement system
analysis.
[0235] Design diagnostics 1920 can evaluate the input design
according to different optimality criterion such as D-efficiency,
G-efficiency, A-efficiency, average variance of prediction and
design creation time. As shown designs can be created very quickly
with good optimality properties (e.g., 100 efficiencies, 0 seconds
to design creation). Output options can also be specified in output
options control 1940 to change the output of the design (e.g.,
where a design is saved, how the runs are displayed, to simulate
responses, etc.). These are merely examples of design diagnostics
and evaluations that can be performed by a computing system. Those
of skill in the art will appreciate other design diagnostic and
evaluation tools for designs generated according to embodiments
herein.
* * * * *