U.S. patent application number 09/753595 was filed with the patent office on 2001-05-24 for database wizard.
Invention is credited to Glumac, Miodrag, Hilemon, Christopher G., Piety, Kenneth R., Reeves, Todd W., Rich, Michael D..
Application Number | 20010001851 09/753595 |
Document ID | / |
Family ID | 22548314 |
Filed Date | 2001-05-24 |
United States Patent
Application |
20010001851 |
Kind Code |
A1 |
Piety, Kenneth R. ; et
al. |
May 24, 2001 |
Database wizard
Abstract
The invention provides a computerized method and apparatus which
enables a user, even one who has little or no predictive
maintenance skills, to establish a predictive maintenance database
that defines information needed to monitor equipment in accordance
with a predictive maintenance plan. The type of equipment
components to be monitored and associated physical characteristics
of the components are input to a computer as an equipment
configuration, which may include one or more interconnected
components. The computer includes a knowledge base that defines
relationships between monitoring practices, component types, and
physical characteristic information for component types. A
predictive maintenance database is constructed for the components
using the inference engine operating on the knowledge base, the
selected component type, and the selected physical characteristic
information. Multiple measurement technologies may be specified for
each component. Preferably, for each measurement technology
specified, the predictive maintenance database includes measurement
points, an analysis parameter set, and an alarm limit set.
Equipment configurations may be defined by the user, or they may be
stored in a configuration/component warehouse with little or no
configuration definition required of the user.
Inventors: |
Piety, Kenneth R.;
(Knoxville, TN) ; Hilemon, Christopher G.;
(Knoxville, TN) ; Reeves, Todd W.; (Knoxville,
TN) ; Glumac, Miodrag; (Knoxville, TN) ; Rich,
Michael D.; (Powell, TN) |
Correspondence
Address: |
LUEDEKA NEELY AND GRAHAM
P O BOX 1871
KNOXVILLE
TN
37901-1871
US
|
Family ID: |
22548314 |
Appl. No.: |
09/753595 |
Filed: |
January 2, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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09753595 |
Jan 2, 2001 |
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09153690 |
Sep 15, 1998 |
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Current U.S.
Class: |
702/184 ;
702/181 |
Current CPC
Class: |
G06N 5/00 20130101 |
Class at
Publication: |
702/184 ;
702/181 |
International
Class: |
G06F 015/00; G06F
017/18 |
Claims
What is claimed is:
1. A method for establishing a predictive maintenance database in a
computer that defines information needed for a user to monitor
components in accordance with a predictive maintenance plan,
comprising: identifying a component type corresponding to a
particular component to be monitored; providing a master file of
information that includes at least component identification
information and corresponding additional information related to the
type of measurements needed by the predictive maintenance plan;
searching the master file for component identification information
corresponding to the identified component type to produce at least
one set of component identification information and selecting a set
of selected component identification information from the at least
one set of component identification information; and constructing
information for a predictive maintenance or the component to be
monitored using the set of selected component identification
information and the additional information corresponding to the set
of selected component identification information.
2. The method of claim 1 wherein said providing step further
comprises: providing a knowledge base in the computer that defines
relationships between monitoring practices, component types and
physical characteristic information for component types; and
providing an inference engine in the computer; and wherein said
constructing step further comprises: operating in part on said
knowledge base with said inference engine to construct database
information.
3. The method of claim 1 wherein said providing step includes
providing component identification information in the form of a
model number for components, and wherein said searching step
includes selecting a best fit set of component identification
information representing the closest relationship between a set of
component identification information and the identified component
type.
4. The method of claim 1 wherein said searching step includes
searching the master file based on model number.
5. The method of claim 1 wherein said searching step includes
searching the master file based on physical criteria of the
component.
6. A method for establishing a predictive maintenance database in a
computer that defines information needed to monitor components in
accordance with a predictive maintenance plan, comprising:
identifying a component type corresponding to a particular
component to be monitored; identifying physical characteristic
information corresponding to the identified component type and the
particular component to be monitored; providing a knowledge base in
the computer that defines relationships between monitoring
practices, component types and physical characteristic information
for component types; providing an inference engine in the computer
for operating in part on said knowledge base to construct
predictive maintenance databases; and constructing information for
a predictive maintenance database for each component to be
monitored using the inference engine operating on the knowledge
base, the selected component type and the selected physical
characteristic information.
7. The method of claim 6 wherein constructing said database
information further comprises defining the type of data to be
measured for the identified component type and defining measurement
points on the component from which data will be measured.
8. The method of claim 6 wherein said constructing step further
comprises defining an analysis parameter set including set-up
parameters for use by a data collection instrument to collect data
in accordance with the predictive maintenance database.
9. The method of claim 6 wherein said constructing step includes
defining an alarm limit set including alarm limits delineating
normal and abnormal component operation for data measured in
accordance with the database.
10. The method of claim 6 wherein said constructing step includes
recommending a plurality of measurement points for measuring
operating characteristics of the component in accordance with the
predictive maintenance plan.
11. The method of claim 10, wherein said constructing step further
comprises specifying a recommended type of component operating
characteristic to be measured at each of said plurality of
measurement points.
12. The method of claim 11, wherein said constructing step further
comprises specifying a recommended alarm limit delineating normal
and abnormal component operation for a recommended type of
component operating characteristic to be measured at a particular
one of the plurality of measurement points.
13. The method of claim 10, further comprising: providing and
displaying an image illustrating the component type; and displaying
locations on the component image corresponding to one or more of
said plurality of measurement points.
14. The method of claim 6, further comprising obtaining data in
accordance with the predictive maintenance database.
15. The method of claim 6, further comprising specifying an
operational significance of the selected component type.
16. The method of claim 6, further comprising editing information
contained in the knowledge base.
17. The method of claim 6, further comprising storing the
identified component type and physical characteristic information
as stored user-defined components, and selecting one of the stored
user-defined components to thereby identify a component type and
physical characteristic information for establishing additional
predictive maintenance databases.
18. The method of claim 6 wherein said identifying steps include:
providing a warehouse containing a list of component types and
associated physical characteristic information; and selecting a
component type from said list.
19. The method of claim 18, further comprising modifying
information contained in the warehouse.
20. The method of claim 6 further comprising specifying a
measurement technology for use in monitoring a component type and
wherein said constructing step produces a predictive maintenance
database corresponding to said measurement technology.
21. A programmable apparatus for establishing a predictive
maintenance database defining information needed to monitor
components in accordance with a predictive maintenance plan, the
apparatus comprising: a memory having a knowledge base defining
relationships between monitoring practices, component types and
physical characteristic information for component types; a data
processor having an inference engine for operating in part on said
knowledge base to construct database information for a predictive
maintenance database; and a user interface for inputting user
commands to the data processor including commands that: (a)
identify a component type corresponding to a particular component
to be monitored; (b) identify physical characteristic information
corresponding to the identified component type and the particular
component to be monitored; and said data processor further
comprising means for constructing information for a predictive
maintenance database for each component to be monitored using the
inference engine operating on the knowledge base, the selected
component type and the selected physical characteristic
information.
22. The apparatus of claim 21 wherein said data processor is
further operable to operate in part on said hueristic knowledge
base to construct a predictive maintenance database defining
measurement points, monitoring schedules, data to be measured, and
alarm limits delineating normal and abnormal component operation to
be applied to the measured data.
23. The apparatus of claim 21 wherein said data processor is
further operable to construct a predictive maintenance database by
defining an analysis parameter set which includes set-up parameters
for use by a data collection instrument to collect data in
accordance with the predictive maintenance plan.
24. The apparatus of claim 21 wherein said data processor is
further operable to construct a predictive maintenance database by
defining an alarm limit set including a plurality of alarm limits
delineating normal and abnormal component operation for data
measured in accordance with the database.
25. The apparatus of claim 21, further comprising: a display
connected to said data processor for displaying the images of the
component types.
26. A method for establishing a predictive maintenance database in
a computer that defines information needed for a user to monitor
components in accordance with a predictive maintenance plan,
comprising: displaying a plurality of component groups, prompting a
user to select at least one of the component groups, and receiving
a user input identifying a selected component group corresponding
to a particular component to be monitored; displaying a plurality
of component types corresponding to the selected group, prompting
the user to select at least one of the component types, and
receiving a user input identifying a selected component type
corresponding to the particular component to be monitored;
displaying physical characteristic types corresponding to the
selected component type, prompting the user to provide physical
characteristic information corresponding to the physical
characteristic types to farther define the particular component to
be monitored, and accepting physical characteristic information
provided by the user; providing a knowledge base in the computer
that defines relationships between monitoring practices and
component types and physical characteristic information for
component types; providing an inference engine in the computer for
constructing database information for a predictive maintenance
database based in part on said knowledge base; using the knowledge
base to select measurement specifications based on the selected
component type and the physical characteristic information provided
by the user; and constructing database information for the
predictive maintenance database for the particular component to be
monitored using the inference engine operating on the knowledge
base, the selected component type, the selected physical
characteristic information, and the user defined measurement
specifications.
27. The method of claim 26, further comprising prompting a user to
accept or modify the measurement specifications and producing user
defined measurement specifications based on the user's response to
said prompting.
28. The method of claim 26, further comprising obtaining predictive
maintenance data in accordance with the predictive maintenance
database constructed for the components to be monitored.
29. The method of claim 28 further comprising the step of comparing
predictive maintenance data to a criterion, and indicating an alarm
condition when the criterion is met.
30. The method of claim 26 further comprising: defining the type of
data to be measured for the identified component type, and
constructing the predictive maintenance database to correspond to
the type of data.
31. The method of claim 26 wherein said constructing step includes
defining an analysis parameter set including set-up parameters for
use by a data collection instrument to collect data in accordance
with the predictive maintenance database.
32. The method of claim 26 wherein said constructing step includes
defining an alarm limit set including alarm limits delineating
normal and abnormal component operation for data measured in
accordance with the database.
33. The method of claim 26, further comprising storing the selected
component type and the selected physical characteristic information
for use in establishing further predictive maintenance
databases.
34. A method for establishing a predictive maintenance database in
a computer that defines information needed for a user to monitor a
configuration of components defined by a plurality of
interconnected components in accordance with a predictive
maintenance plan, comprising: defining a configuration of
components by: displaying a first plurality of component groups,
prompting a user to select at least one of the component groups to
produce a first selected group, receiving a user input identifying
a first selected component group, corresponding to a first
component in the configuration; displaying a first plurality of
component types corresponding to the first selected group,
prompting the user to select at least one of the component types to
produce a first selected component type, receiving a user input
identifying a first selected component type corresponding to the
first component; displaying physical characteristic types
corresponding to the first selected component type, prompting the
user to provide physical characteristic information corresponding
to the physical characteristic types to further define the first
selected component type, accepting physical characteristic
information provided by the user; displaying a second plurality of
component groups, prompting a user to select at least one of the
component groups to produce a second selected group, receiving a
user input identifying a second selected component group
corresponding to a second component in the configuration;
displaying a second plurality of component types corresponding to
the second selected group, prompting the user to select at least
one of the component types to produce a second selected component
type, receiving a user input identifying a second selected
component type corresponding to the second component; displaying
physical characteristic types corresponding to the second selected
component type, prompting the user to provide physical
characteristic information corresponding to the physical
characteristic types to further define the second selected
component type, accepting physical characteristic information
provided by the user corresponding to the second selected component
type; displaying a plurality of spatial orientations,
interconnection arrangements, and types of couplings, prompting the
user to select a spatial orientation, interconnection arrangement,
and coupling type, receiving user inputs identifying a selected
spatial orientation, interconnection arrangement, and coupling type
to define a selected configuration corresponding to a physical
interconnection between the first and second selected component
types; providing a knowledge base in the computer that defines
relationships between monitoring practices and configuration types
and physical characteristic information for configuration types;
providing an inference engine in the computer for constructing
database information for a predictive maintenance database based in
part on said knowledge base; using the knowledge base to select
measurement specifications based on the selected configuration and
the physical characteristic information provided by the user; and
constructing database information for the predictive maintenance
database for the particular configuration to be monitored using the
inference engine operating on the knowledge base, the selected
configuration, the selected physical characteristic information,
and the user defined measurement specifications.
35. The method of claim 34, further comprising storing the selected
configuration, the selected physical characteristic information,
and the user defined measurement specifications for establishing
further predictive maintenance databases.
36. A data processing apparatus for defining component
configurations, the data processing apparatus including a user
interface for receiving commands and data from a user and
comprising: a component design studio for displaying a plurality of
component types, for responding to user inputs, to select a
component type and for displaying the selected component type; a
first user interface screen responsive to user input and the
selected component type for prompting the user to provide physical
parameter information related to the selected component type and
for accepting parameter information provided by the user; and
processing means for defining a component configuration based upon
at least the selected component type and the parameter
information.
37. The apparatus of claim 36 further comprising: a second user
interface screen responsive to the selected component type for
prompting the user to provide analysis information related to
analysis of data and for accepting the analysis information that is
input by the user; and wherein said processing means is responsive
to the selected component type, the parameter information and the
analysis information for defining a component configuration.
38. The apparatus of claim 36 wherein said component design studio
further comprises means for displaying a plurality of predictive
measurement technologies and for responding to user inputs to
select at least one predictive maintenance technology.
39. The apparatus of claim 38 when said first user interface screen
is responsive to the selected predictive maintenance technology for
prompting the user to provide parameter information corresponding
to the selected predictive maintenance technology.
40. The apparatus of claim 36 further comprising: a second user
interface screen responsive to user input and the selected
component type for prompting the user to provide analysis
information related to analysis of data and for accepting analysis
information; said processing means being responsive to the selected
component type, the parameter information and the analysis
information for defining a component configuration; said component
design studio further comprising means for displaying a plurality
of predictive maintenance technologies and for responding to user
inputs to select at least one predictive maintenance technology;
and said first user interface screen and said second user interface
screen being responsive to the selected predictive maintenance
technology for prompting the user to provide, respectively,
parameter information and analysis information corresponding to the
selected predictive maintenance technology.
41. The apparatus of claim 36 wherein said computer design studio
graphically displays a plurality of component types including
couplings for being selected by a user, selects a plurality of
component types based on user input, graphically displays the
plurality of selected component types, graphically represents the
position of each selected component type to each of the other
selected component types and graphically represents couplings
connected between other component types.
42. A method for graphically associating a plurality of machine
components in a computer to define an equipment configuration for
use in establishing a predictive maintenance database for the
equipment configuration, comprising: identifying a first component
type corresponding to a first component to be monitored, said first
component type having a plurality of first physical component
parameters; specifying first component information corresponding to
one or more of said plurality of first physical component
parameters; producing a first component configuration from the
identified first component type and first component information;
identifying a second component type corresponding to a second
component to be monitored, said second component type having a
plurality of second physical component parameters; specifying
second component information corresponding to one or more of said
plurality of second physical component parameters; producing a
second component configuration from the identified second component
type and second component information; defining a physical coupling
between the first component type and the second component type; and
producing an equipment configuration from the first component
configuration, the second component configuration, and the physical
interconnection.
43. The method of claim 42, further comprising: associating with
the first component type at least one of a plurality of measurement
technologies to produce one or more selected measurement
technologies; and constructing a predictive maintenance database
based on the identified first component type, the first component
information, and said one or more selected measurement
technologies.
44. A method for establishing a predictive maintenance database in
a computer that defines information needed to monitor components in
accordance with a predictive maintenance plan, comprising:
identifying a component type corresponding to a particular
component to be monitored; identifying physical characteristic
information corresponding to the identified component type and the
particular component to be monitored; providing a set of rules in
the computer which define relationships between monitoring
practices, component types and physical characteristic information
for component types; associating a type of data to be collected
with the identified component type and the identified physical
characteristic information; and constructing a predictive
maintenance database for the particular component to be monitored
using the set of rules operating on the identified component type,
the identified physical characteristic information, and the type of
data to be collected.
45. The method of claim 44 wherein said step of associating
includes associating a vibration measurement technology as the type
of data to be collected.
46. A programmable apparatus for establishing a predictive
maintenance database defining information needed to monitor
components in accordance with a predictive maintenance plan, the
apparatus comprising: a memory having a master file of information
in the computer that includes at least component identification
information and corresponding predictive maintenance database
information that specifies the types of measurements needed by the
predictive maintenance plan for each component in the master file;
a user interface for inputting user commands including commands
that provide component identification information and thereby
identify components to be monitored; a data processor for receiving
commands from the user interface and having a search engine for
searching the master file for component identification information
corresponding to an identified component to produce at least one
set of component identification information and selecting a set of
selected component identification information from the at least one
set of component identification information; and said data
processor further comprising means for constructing database
information for a predictive maintenance database for an identified
component to be monitored using the set of selected component
identification information and the predictive database information
corresponding to the set of selected component identification
information.
47. The apparatus of claim 46 wherein said component identification
information is selected from the following group: a manufacturer
name for components, a model number for component, and physical
criteria corresponding to physical characteristics of
components.
48. The apparatus of claim 46 wherein said predictive database
information is selected from the following group: a type of
measurement analysis to be performed in accordance with the
predictive maintenance plan, measurement point locations
identifying points on components where predictive maintenenance
data is to be measured in accordance with the predictive
maintenance plan, and spectral analysis parameters to be employed
for collection of spectral data.
49. The apparatus of claim 46 wherein said search engine further
comprises means for automatically selecting a best fit set of
component identification information representing the closest
relationship between a set of component identification information
and the identified component type.
50. The apparatus of claim 46 wherein said search engine further
comprises means for producing a plurality of records of component
identification information and selecting a best fit set of
component identification information from the plurality of records
of component identification information.
51. The apparatus of claim 46 wherein said search engine further
comprises means for searching the master file based on at least one
of the following group: manufacturer name, model number, and
physical criteria of the component.
Description
TECHNICAL FIELD
[0001] The present invention relates generally to predictive
maintenance of machines. More particularly, it relates to
establishing and executing predictive maintenance programs for
machines.
BACKGROUND
[0002] A highly specialized art, commonly referred to as
"predictive maintenance", has evolved in an effort to predict when
a machine will fail or requires maintenance so that corrective
measures can be taken on an "as needed" basis. In a typical
predictive maintenance program, periodic inspections of the
machinery are made in order to assess the current operating
condition of the machine. Data collected during the periodic
inspections is typically stored and trended over time as an
additional measure of the machine's operational health. The
periodic inspections often involve multiple technologies including
oil sampling and analysis, infrared thermography analysis, and
vibrational analysis. For example, vibrational analysis is usually
accomplished with the aid of a portable instrument which senses and
processes vibration generated by the machine. These portable
instruments, which are often referred to as data collectors or data
analyzers, typically include a vibration transducer attached to
what is essentially a highly specialized hand-held computer. The
maintenance technician places the vibration transducer against a
predefined test point of the machine. In a typical application, the
resultant machine vibration signal produced by the transducer is
provided to the data collector where the data is processed (and
perhaps analyzed) according to predefined conditions and stored for
later downloading to a machine database which has been previously
set up on a host computer. The host computer analyzes the vibration
data for faults or other anomalous conditions and machine data is
stored in the database.
[0003] Machines within a facility are typically monitored according
to a route which is generated by a maintenance technician and
programmed into the data collector with the aid of the host
computer. The route typically includes a list of machines,
measurement test points, and setup conditions for each test point.
There are usually many machines in the route with many test points
on each machine, and for each test point there may be specified a
vibration frequency range to be analyzed, a type of analysis to be
performed, a particular type or set of data to be measured and
stored, and similar other data collection and analysis parameters.
In response to commands from the user, the hand held instrument
prompts the user for the identity of the machine and the test point
to be monitored, and it automatically sets up the instrument, for
example, to accept the specified frequency range for the test
point, perform the specified analysis and store the specified type
or set of data. A Fast Fourier Transform analysis may be performed
on a pre-selected frequency range of the data and all or part of
the resulting frequency spectrum may be stored and displayed. As
the user progresses through the many machines and the corresponding
test points, he collects and stores measured data which is
subsequently transferred to the host computer for long term storage
and further analysis.
[0004] In a typical predictive maintenance program as described
above, the user or maintenance technician defines each test point
for each machine, including the location for each test point,
analysis parameters sets including the type and quantity of data to
take for each test point, the type of analysis to be performed on
the data, frequency ranges, alarm levels, and the like. The
locations of the test points and the parameters or settings chosen
for each test point vary depending on the type, size and
combinations of machines. For example, a motor driving a fan may
have different test points and settings than the same motor driving
a pump. Defining a predictive maintenance program of this type for
such a large number of different machines requires a great deal of
expertise on the part of the maintenance technician. Such expertise
is typically gained only through years of experience and/or
extensive training including training on how to select a
probe/sensor, which parameters to measure for a particular machine,
locations corresponding to the most data rich points for measuring
particular parameters, frequency ranges, analysis alarm limits, and
other such information. The maintenance technician must define the
measurements points, analysis parameter sets, and alarm limit sets,
and he must integrate these items into a useable database setup.
Further complicating the maintenance technician's task is the fact
that knowledge and expertise gained through the experience of
engineers and technicians is too often not shared among users and
others skilled in the art. More common is the situation where
expertise is lost as a result of job changes and the like. Even
when expertise is documented in user's manuals or lab notebooks, a
great deal of studying and understanding is required in order for
the information to be put to proper use.
[0005] The difficulty and complexity of providing the settings for
each measurement point may be more fully appreciated by considering
the settings disclosed for measurement point examples in the
following detailed description. While a relatively small number of
examples are discussed herein, it will be understood that a typical
predictive maintenance database will have hundreds of measurement
points with each measurement point typically having multiple
settings. In predictive maintenance databases involving vibration,
each measurement point will typically have many settings, often 50
or more.
[0006] Clearly, the proficiency and skill required to define an
acceptable predictive maintenance program for a machine is beyond
the abilities of a layperson. Because of the complexity involved
and the expertise needed to properly set up an adequate predictive
maintenance program or model, data technicians have been known to
simply use the default settings provided by vendors of data
collection/analysis instruments.
SUMMARY
[0007] What is needed, therefore, is a simplified method and
apparatus for creating a predictive maintenance database. The
solution should include the integration of machine database setup
with the use of multiple measurement technologies, and it should be
capable of pooling the scattered expertise of predictive
maintenance modelers and significantly reducing the level of skill
and expertise needed to establish an adequate predictive
maintenance program.
[0008] With regard to the foregoing and other objects, the
invention in one aspect provides a method for establishing a
predictive maintenance database that defines information needed to
monitor components in accordance with a predictive maintenance
plan. In the method, a component type corresponding to a particular
component to be monitored is identified, and physical
characteristic information is also identified which corresponds to
the identified component type and the particular component to be
monitored. A knowledge base in the computer defines relationships
between monitoring practices, component types and physical
characteristic information for component types, and an inference
engine operates in part on the knowledge base to construct
predictive maintenance databases. Information for a predictive
maintenance database is constructed for each component to be
monitored using the inference engine operating on the knowledge
base, the selected component type and the selected physical
characteristic information.
[0009] In accordance with the above method, the database
information for the predictive maintenance database may be
constructed in a number of ways based on a number of factors. For
example, the database information may be constructed to correspond
in part to a defined type of data to be measured for the identified
component type and/or defined measurement points on the component
from which data will be measured. An analysis parameter set may
also be defined by the constructing step, including setup
parameters for use by a data collection instrument to collect data
in accordance with the predictive maintenance database.
Additionally, an alarm limit set may be defined by the constructing
step including alarm limits delineating normal and abnormal
component operation for data measured in accordance with the
database.
[0010] Further, constructing the database information may include
recommending a plurality of measurement points for measuring
operating characteristics of the component in accordance with the
predictive maintenance plan.
[0011] Additional steps in the method may include specifying a
recommended type of component operating characteristic to be
measured at each of the plurality of measurement points, and
specifying a recommended alarm limit delineating normal and
abnormal operation for a recommended type of component operating
characteristic to be measured at a particular one of the plurality
of measurement points. An image of the component being monitored
may be displayed with locations being designated on the component
image corresponding to one or more of the plurality of measurement
points. An operational significance of the selected component type
may be specified. The selected component type and the selected
physical characteristics may be stored and later used for
establishing further predictive maintenance databases. When
collecting data in accordance with the predictive maintenance
program, a user is prompted to place an appropriate measurement
device at a measurement point to measure a component operating
characteristic.
[0012] The present invention also provides a programmable apparatus
for establishing a predictive maintenance database defining
information needed to monitor components in accordance with a
predictive maintenance plan. In the apparatus, a knowledge base
defines relationships between monitoring practices, component types
and physical characteristic information for component types. A data
processor is connected to the memory and includes an inference
engine for operating in part on the knowledge base to construct
database information for a predictive maintenance database. Based
on user input provided through a user interface, the data processor
is programmed to identify a component type corresponding to a
particular component to be monitored, identify physical
characteristic information corresponding to the identified
component type and the particular component to be monitored, and
construct information for a predictive maintenance database for
each component to be monitored using the inference engine operating
on the knowledge base, the identified component type and the
identified physical characteristic information.
[0013] In accordance with another aspect of the preferred method, a
plurality of component groups are displayed, and a user is prompted
to select at least one of the component groups. User input
identifies a selected component group corresponding to a particular
component to be monitored. A plurality of component types is
displayed corresponding to the selected group, and the user is
prompted to select at least one of the component types. A user
input is received identifying a selected component type
corresponding to the particular component to be monitored. Physical
characteristic types are displayed corresponding to the selected
component type, and the user is prompted to provide physical
characteristic information corresponding to the physical
characteristic types to further define a component to be monitored.
Physical characteristic information provided by the user is
accepted. A knowledge base is provided in the computer and defines
relationships between monitoring practices and component types and
physical characteristic information for component types. An
inference engine is also provided in the computer for constructing
database information for predictive maintenance databases based in
part on the knowledge base. The knowledge base is used to select
measurement specifications based on the selected component type and
the physical characteristic information provided by the user. The
database information is constructed using the inference engine
operating on the knowledge base, the selected component type, the
selected physical characteristic information, and the user defined
measurement specifications.
[0014] The present invention also provides a method for associating
a machine component with physical information in a computer to
define a component configuration for use in establishing a
predictive maintenance database for the component configuration.
This method involves identifying a component type corresponding to
a particular component to be monitored where the particular
component to be monitored has a plurality of physical component
parameters. Information is specified which corresponds to one or
more of the plurality of physical component parameters. For
example, the specified information may include the operating speed
of the particular component to be monitored. A component
configuration is produced from the identified component type and
the specified information.
[0015] The method may further include the steps of associating with
the component type at least one of a plurality of measurement
technologies. Database information for predictive maintenance
database is then constructed based on the identified component
type, the specified information, and said one or more selected
measurement technologies.
[0016] Another method according to the present invention involves
graphically associating a plurality of machine components in a
computer to define an equipment configuration for use in
establishing a predictive maintenance database for the equipment
configuration. In this method, a first component type is identified
which corresponds to a first component to be monitored. A second
component type is likewise identified which corresponds to a second
component to be monitored. For each of the first and second
component types, component information is specified which
corresponds to physical characteristics of the component. Component
configurations are produced for each of the first and second
components based on the specified component types and component
information. A physical coupling between the two components is
defined, and an equipment configuration is produced from the two
equipment configurations and the physical coupling.
[0017] In accordance with a further aspect of the invention, there
is provided a method for establishing a predictive maintenance
database in a computer that defines information needed to monitor
components in accordance with a predictive maintenance plan. The
method includes identifying a component type corresponding to a
particular component to be monitored. A master file of information
is provided in the computer which includes at least component
identification information and corresponding predictive maintenance
database information that specifies the types of measurements
needed by the predictive maintenance plan for each component in the
master file. The master file is searched for component
identification information corresponding to the identified
component type to produce at least one set of component
identification information, and a set of component identification
information is selected from the at least one set of component
identification information. Finally, database information is
constructed for a predictive maintenance database for the component
to be monitored using the set of selected component identification
information and the predictive database information corresponding
to the set of selected component identification information.
[0018] The master file of information may include component
identification information in the form of the name of component
manufacturers, component model numbers, or both. The component
identification information may also physical criteria corresponding
to physical characteristics of components.
[0019] Predictive maintenance database information may include
measurement point locations identifying points on components where
predictive maintenance data is to be measured in accordance with
the predictive maintenance plan. Types of measurement analyses to
be performed in accordance with the predictive maintenance plan may
also be included as predictive maintenance database
information.
[0020] Searching of the master file for component identification
information may be accomplished by searching based on manufacturer
name and model number. The master file may also be searched based
on physical criteria of the component.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The above and other features, aspects and advantages of the
present invention will now be discussed in the following detailed
description and appended claims considered in conjunction with the
accompanying drawings in which:
[0022] FIG. 1 is a functional block diagram of a programmable
apparatus for use in establishing a predictive maintenance database
in accordance with the present invention;
[0023] FIG. 2 is a flow diagram of a program for controlling
operation of the apparatus of FIG. 1;
[0024] FIG. 3 is a user interface screen which enables the user to
initiate creation of a new predictive maintenance database and to
initiate modification of an existing predictive maintenance
database in accordance with the present invention;
[0025] FIG. 4 is a user interface screen prompting the user to
select predefined predictive maintenance technologies to be
incorporated into the creation of a new predictive maintenance
database;
[0026] FIG. 5 is a user interface screen prompting the user to
define information corresponding to the location of equipment to be
monitored in accordance with the new predictive maintenance
database being created;
[0027] FIG. 6 is a user interface screen prompting the user to
define information corresponding to the type of equipment to be
monitored and the operational significance of the equipment;
[0028] FIG. 7 is a user interface screen prompting the user to
define information for use by the program of FIG. 2 in creating
vibration monitoring elements of the predictive maintenance
database;
[0029] FIG. 8 is a user interface screen prompting the user to
define information for use in creating thermography monitoring
elements of the predictive maintenance database;
[0030] FIG. 9 is a user interface screen of a main program screen
which provides a tree structure of database files and enables the
user to launch a detailed configuration of equipment represented by
a file shown in the tree structure;
[0031] FIG. 10 is a user interface screen from which the user has
initiated definition of a new equipment configuration file entitled
"Boiler Feed Pump #1" defining physical characteristics of a boiler
feed pump to be monitored;
[0032] FIG. 11 is a user interface screen for defining components
and component connectivity of the new configuration;
[0033] FIG. 12 is a user interface screen prompting the user to
define general motor configuration information;
[0034] FIG. 13 is a user interface screen prompting the user to
define information corresponding to motor type;
[0035] FIG. 14 is a user interface screen prompting the user to
define physical characteristics of the motor;
[0036] FIG. 15 is a user interface screen prompting the user to
define bearing types;
[0037] FIG. 16 is a user interface screen which enables the user to
select bearing information from a list of known bearings;
[0038] FIG. 17 is a user interface screen from which known bearings
can be looked up and imported into the component definition;
[0039] FIG. 18 is a user interface screen showing motor vibration
measurement points selected by the program of FIG. 2;
[0040] FIG. 19 is a user interface screen showing that the motor is
configured for the vibration technology selected in FIG. 4 and the
user is initiating configuration of the motor for another
technology;
[0041] FIG. 20 is a user interface screen prompting the user for
general motor information;
[0042] FIG. 21 is a user interface screen prompting the user for
information corresponding to rated operating parameters of the
motor;
[0043] FIG. 22 is a user interface screen showing motor measurement
points selected by the program of FIG. 2;
[0044] FIG. 23 is a user interface screen in which the user
initiates configuration of the motor for thermography data
collection;
[0045] FIG. 24 is a user interface screen which enables the user to
provide information for use in selecting thermography measurement
points for the motor;
[0046] FIG. 25 is a user interface screen which enables the user to
initiate configuration of the pump for vibration technology;
[0047] FIG. 26 is a user interface screen prompting the user for
general pump information;
[0048] FIG. 27 is a user interface screen prompting the user for
additional pump information;
[0049] FIG. 28 is a user interface screen showing pump vibration
measurement points selected by the program of FIG. 2;
[0050] FIG. 29 is a user interface screen showing the pump and
motor components as being configured with an unconfigured coupling
between the pump and motor;
[0051] FIG. 30 is a user interface screen showing the pump, motor,
and coupling as being configured;
[0052] FIG. 31 is a user interface screen from which the user may
initiate creation of a predictive maintenance database in
accordance with the invention;
[0053] FIG. 32 is a user interface screen showing the progress of
the program of FIG. 2 while creating a predictive maintenance
database;
[0054] FIG. 33 is a user interface screen of a main program screen
for a database viewing program which enables viewing and editing of
information contained in predictive maintenance database files
created in accordance with the present invention;
[0055] FIG. 34 is a user interface screen of the database viewing
program which displays measurement points created by the main
program;
[0056] FIG. 35 is a user interface screen of the database viewing
program showing detailed measurement point information;
[0057] FIG. 36 is a user interface screen of the database viewing
program showing additional detailed measurement point
information;
[0058] FIG. 37 is a user interface screen of the database viewing
program showing a list of analysis parameters created by the main
program;
[0059] FIG. 38 is a user interface screen of the database viewing
program showing analysis parameter details for time waveform and
frequency spectrum data collection;
[0060] FIG. 39 is a user interface screen of the database viewing
program showing additional analysis parameter details for frequency
ranges in which measurements are taken;
[0061] FIG. 40 is a user interface screen of the database viewing
program showing a list of alarm limits which correspond to the
analysis parameters of FIG. 37;
[0062] FIG. 41 is a user interface screen of the database viewing
program showing amplitude limits defining multiple levels of
alarming;
[0063] FIG. 42 is a user interface screen from which the user has
initiated definition of a new equipment configuration file entitled
"Condensate Pump #1" defining physical characteristics of a
condensate pump to be monitored;
[0064] FIG. 43 is a user interface screen prompting the user to
specify whether existing configuration information will be used to
configure the condensate pump;
[0065] FIG. 44 is a user interface screen which enables the user to
select from a list of known motor types for use in configuring the
condensate pump;
[0066] FIG. 45 is a user interface screen prompting the user for
general information corresponding to the condensate pump motor;
[0067] FIG. 46 is a user interface screen for displaying motor
construction information;
[0068] FIG. 47 is a user interface screen for displaying motor
bearing information;
[0069] FIG. 48 is a user interface screen for displaying additional
bearing information;
[0070] FIG. 49 is a user interface screen showing motor measurement
points;
[0071] FIG. 50 is a user interface screen showing configured and
unconfigured components of the condensate pump;
[0072] FIG. 51 is a user interface screen showing information
corresponding to a component of the condensate pump;
[0073] FIG. 52 is a user interface screen showing a completely
configured condensate pump;
[0074] FIG. 53 is a table of candidate measurement points for an AC
induction motor showing how operational significance relates to
measurement point selection; and
[0075] FIG. 54 is a user interface screen showing a master
component/database file and related data fields for constructing a
predictive maintenance database using a lookup technique.
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT
[0076] With reference now to the drawings in which like reference
characters designate like or similar parts throughout the several
views, FIG. 1 shows in block diagram form a programmable apparatus
10 for establishing a predictive maintenance database defining
information needed to monitor equipment (such as electric motors,
pumps, bearings, and the like) for anomalous or faulty operation in
accordance with a predictive maintenance plan. The apparatus 10
includes a computer 20 having a data processor 22 and associated
memory 28, a display 26, and a user interface 24 (such as a mouse
and/or keyboard) for inputting user information and commands to the
data processor 22. A communications port 30, such as a serial
infrared data port, is provided to enable the computer 20 to
communicate with a peripheral device. When images of monitored
equipment are desired, a digital camera 32 may be provided for
capturing digital images of equipment which are provided to the
data processor 22 for processing and display on the display 26.
[0077] In a preferred embodiment, the data processor 22 is a
Pentium.TM. processor running Windows.TM. 95 or Windows.TM. NT
Workstation as the operating system. When programmed in accordance
with the invention, the computer 20 provides a straightforward,
user-friendly environment in which users, including inexperienced
maintenance technicians, can quickly and efficiently create a
predictive maintenance database or create an area or component that
may be incorporated into an existing predictive maintenance
database.
[0078] As an initial matter, defining a few terms commonly used
herein should be helpful. By use of the term "area" herein, what is
meant is a location within a site such as a building. A "site" is a
grouping of areas. A site is usually equated with a plant. A
"component" is an item to be monitored in accordance with
appropriate predictive maintenance principles. Motors, engines,
gearboxes, pumps, fans, and like items are examples of components.
A "configuration" is a detailed definition of one component or of
two or more interconnected components, such as a motor driving a
pump. "Equipment" is rather broadly defined as including
components, groups of components, and configurations. For rotating
machinery, the term "equipment" may be used to refer to a machine
train consisting of components connected via a coupling.
"Operational Significance" is a general rating that indicates the
relative criticality of proper operation of equipment to the
activities of the site. Values for operational significance include
"critical", "essential", "important", "secondary", and
"non-essential".
[0079] Operation of the computer 20 to establish a predictive
maintenance database in accordance with a preferred embodiment of
the invention is illustrated in the flow diagram of FIG. 2
(hereinafter referred to as the "program 40"). As will be seen, the
program 40 combines user input with stored information to create
new predictive maintenance database files, or to modify or edit an
existing predictive maintenance database file, in a fraction of the
time and effort previously required with conventional
approaches.
[0080] The program includes three major subsystems--a main program
42, a component design studio 44, and an inference engine 46. The
main program 42 accepts site information from the user 48 and
assists the user 48 in describing the site information. It leads
the user 48 through the creation of areas and equipment that match
the topology of the site to be monitored. The user 48 may also
provide information relative to the monitoring technologies to be
applied and the monitoring hardware to be used as part of the
predictive maintenance plan implemented by the predictive
maintenance database.
[0081] The component design studio 44 is a drag-and-drop graphical
user interface tool that enables the user 48 to define new
equipment and edit existing equipment, including components and
configurations, for predictive maintenance databases. It enables
the user 48 to graphically associate machine components, such as a
pump being driven by a motor. Component and configuration
definitions can be stored and reused for further equipment
definitions. The component design studio 44 also prompts the user
48 for information relating to physical characteristics of the
equipment to be monitored. For example, if the equipment to be
monitored is an electric motor, physical characteristic information
would preferably include the spatial orientation of the motor
(i.e., vertical or horizontal), number of poles, number of stator
bars, rated running speed, and other information. The physical
characteristics information may also be stored and reused for
further equipment definitions.
[0082] In a preferred embodiment, the graphical user interface
(GUI) is a standard Microsoft Windows.TM. Single Document Interface
(SDI) application. The application includes a main window that may
contain one or more data views. One of these views is a tree that
shows the hierarchy of items created by the user. Other views,
based on the currently selected tree item, show the properties of
the selected item and provide access to other user interfaces which
aid the user 48 in creating and modifying equipment definitions and
databases.
[0083] Common configurations and component definitions may be
stored in one or more component warehouse databases 64 which can be
accessed by the component design studio 44, thus eliminating the
need for the user 48 to create configuration and component
definitions for equipment that has been previously defined and
stored in a component warehouse database 64. For configurations,
component identifiers and connectivity information are stored in
the warehouse 64. For components, the physical characteristics are
stored in the warehouse 64. In a preferred embodiment, two types of
warehouses are available to the user 48. One type of warehouse is a
read-only warehouse containing a large number of ready to use
component and/or configuration definitions and is provided by the
program supplier as an integral part of the program 40. A second
type of warehouse is one that is created by the user 48 for storing
configurations and components not included in the read-only
warehouse.
[0084] The main program 42 and component design studio 44 produce a
single program data file 50 that includes information entered by
the user 48 including site information and equipment definition
information. This information may be modified by the user 48 until
the user 48 is satisfied with its completeness and accuracy. At
that time, the user 48 may request that the program data file 50 be
passed on to the inference engine 46.
[0085] The inference engine 46 performs the task of taking user
input relating to the physical characteristics of components and
combining it with predictive maintenance knowledge to determine the
best monitoring setup for those components. In this regard, the
inference engine 46 receives the program data file 50 and creates a
predictive maintenance database file by operating on data contained
in the program data file 50 in conjunction with data and
information contained in a knowledge base 52. Information contained
in the predictive maintenance database file may be viewed and
edited by a database program 54, which in a preferred embodiment is
an RBMware.TM. predictive maintenance database program available
from Computational Systems, Inc. of Knoxville, Tenn. The inference
engine 46 also enables the user to edit existing predictive
maintenance database files 54.
[0086] The knowledge base 52 contains empirical knowledge and
information gained through years of experience in monitoring
equipment. The primary references which had the most significant
impact on the contents of the knowledge base 52 include
observations of "best practices" by engineers experienced in the
art of predictive maintenance. The various teachings of numerous
text books and industry periodicals also shaped the contents of the
knowledge base 52, including that portion of the knowledge base 52
which generates analysis parameter sets and alarm limit sets.
Accordingly, predictive maintenance databases created by the
program 40 will far exceed the quality of databases created by a
novice, and will likewise deliver the experienced user 48 quality
databases which have been standardized across equipment types.
[0087] The information contained in the knowledge base 52 defines a
relationship between physical characteristics of a component type
and optimal monitoring practices as determined from experience. For
example, information in the knowledge base 52 includes measurement
points for placement of sensors, types of sensors to place at the
measurement points, and setup criteria for the measurement
instrument (such as number of frequency lines, frequency filtering
of sensor signals, frequency bands of interest, alarm limits, and
the like). The number and types of measurement points created
depends on the component's type and operational significance. For
equipment which includes bearings, there will typically be three
vibration measurement points per bearing located in each of the
three directions (horizontal, vertical, and axial). Information
contained in the knowledge base 52 may be edited to reflect
advances in predictive maintenance know-how.
[0088] Analysis parameter sets, defining the types and amounts of
data to be measured, setup criteria, and the like, are partially
predefined in the knowledge base. The analysis parameter sets are
different for different types of components and are used to setup a
data collection instrument to collect data in an optimal way. The
predefined analysis parameter sets are applicable for most
components of a given type (motors, turbines, pumps, fans, and the
like) and are organized into families of similar components.
[0089] For each of the component types, a core group of sleeve and
rolling element bearing analysis parameter sets exist. These
analysis parameter sets are somewhat dependent on turning speed.
The selection of a specific analysis parameter set is based on
component type, measurement point RPM, bearing type, and similar
information. The analysis parameter sets typically are unaffected
by adjacent components and coupling information.
[0090] For rotating equipment, the number of lines of resolution
for frequency analysis is variable based on the reference turning
speed of the equipment. This enables the user 48 to have good
frequency resolution over a wide range of operating speeds.
[0091] Alarm limit sets, defining criteria for generating fault
alarms for parameters contained in the analysis parameter sets, are
grouped by components similar to the analysis parameter sets. They
are based on the type of equipment being monitored and the type of
environment in which the equipment is operated. Each component type
includes a family of alarm limit sets with each alarm limit family
having a plurality of alarm grades, and some families may be
accessed by multiple components. For example, in one embodiment, a
family of alarm limit sets for AC induction motors is also used by
AC synchronous motors. In addition, some alarm values are based on
several factors such as driven component type, component RPM, and
component weight and horsepower. Different levels of alarm severity
are also assigned for some component types.
[0092] The selection of an alarm limit set is also effected by
alarm criteria which may be input by the user. Alarm criteria is
preferably based on the operating environment of the equipment.
Values for alarm criteria include "smooth", "moderate", and "rough"
where a "smooth" alarm criteria represents a low threshold for
alarming and "rough" represents a high threshold for alarming.
[0093] The inference engine 46 provides the capability for
integration of multiple measurement technologies including
vibration monitoring technology, motor monitoring technology, oil
monitoring technology 56 and thermography 60. In operation, the
program 40 prompts the user 48 to specify a particular type of
measurement technology to be applied. The user 48 provides
information relative to the specified measurement technology, and
the program 40 creates a predictive maintenance database file
applying the measurement technology to a specified piece of
equipment to be monitored. Multiple measurement technologies may be
applied to each piece of equipment.
[0094] Data analysis for purposes of predictive maintenance is
highly dependent on the quality of the collected data.
Unfortunately, many new users 48 do not have the knowledge and
experience needed to properly configure a predictive maintenance
database to be able to perform beneficial analysis. The present
invention provides a user interface to the program 40 which enables
the user 48 to easily answer questions and to provide information
used by the program 40 in constructing predictive maintenance
database files. The program 40 is designed to "fail safe" in the
sense that, no matter how much or how little information the user
48 knows about the equipment, the program 40 is able to create
meaningful analysis parameter sets and alarm limit sets for quality
data collection.
[0095] A computer 20 running a program 40 in accordance with the
invention receives the user inputs and creates a predictive
maintenance database complete with equipment definition
information, measurement points, analysis parameter sets, alarm
limit sets, and equipment configuration information needed for
expert analysis systems to perform detailed analysis on data that
is collected. The following Example 1 describes a typical process
for creating a predictive maintenance database for a boiler feed
pump located within a site. Typically, a separate predictive
maintenance database file is created for each site and each piece
of equipment to be monitored within the site is included in the
database file. Example 2 describes how the program enables the user
48 to reuse previously configured components when adding new
equipment configurations to the database or editing existing
equipment configurations in the database. Example 3 provides a
detailed explanation of how the inference engine 46 creates
analysis parameter sets, alarm limit sets, and measurement points
based on rules defined in the knowledge base 52 and/or the
inference engine 46. As will be seen from the following examples,
the program 40 enables the user 48 to successfully create a
predictive maintenance database even if the user 48 knows very
little about the equipment and needs to select the default answers
provided by the program 40.
[0096] In considering the knowledge base 52 and the inference
engine 46, it is important to understand that there is not a unique
correct way to monitor equipment. There are many correct ways to
monitor a particular piece of equipment or a particular
configuration, and there are many incorrect ways to do so. Thus,
the knowledge base 52 and the inference engine 46 may vary widely,
so long as they operate together to specify one of the correct
setups for monitoring the specified equipment. The examples
discussed below illustrate a correct knowledge base 52 and
inference engine 46, but the examples should not be construed as
limiting the invention to any one knowledge base 52 or inference
engine 46. In particular, rules for defining measurement points,
analysis parameter sets, and alarm limit sets may exist in either
or both of the knowledge base 52 and inference engine 46.
EXAMPLE 1
[0097] FIGS. 3-32 are images of user interface screens, or screen
shots, of a typical "run" through the program 40 for creating a
predictive maintenance database for a boiler feed pump. The screens
are displayed to the user 48 at display 26 and generally prompt the
user 48 for information which assists the program 40 in creating
the database.
[0098] In FIG. 3, the user 48 is asked whether he wishes to create
a new predictive maintenance database or open an existing database
for editing. Existing database files which the user 48 may select
are listed in window 70. In this example, the user 48 has opted to
create a new database.
[0099] To create the new database, the program 40 will need to know
which measurement technologies should be applied and which types of
measurement hardware should be used. FIG. 4 prompts the user 48 to
provide this information. In window 72, the user 48 is asked to
specify measurement technologies and in window 74 the user 48 is
asked to specify measurement hardware information. In FIG. 4, the
user 48 has selected all measurement technologies including
periodic vibration, oil analysis, thermography, MotorView.TM. (an
electrical motor analysis program available from Computational
Systems, Inc. of Knoxville, Tenn.), and corrective analysis (a
group of monitoring technologies available from Computational
Systems, Inc. under the trade name UltraManage.TM.). In window 74,
the user 48 has selected "periodic vibration". This selection takes
the user 48 to another screen from which the user 48 selects
specific periodic vibration hardware.
[0100] The user 48 is next prompted in FIG. 5 to provide an area
name 76 and an area identification 78. The area name 76 is
typically a descriptive word name for the facility, such as
"powerhouse", and the area identification 78 is typically a
shortened term or group of characters that the user 48 can
identify. Here, the user 48 has chosen the identification "PH01" to
represent powerhouse #1.
[0101] In FIG. 6, the user 48 provides an equipment name 80, an
equipment identification 82, and an operational significance 84. As
described above, values for operational significance of equipment
include "critical", "essential", "important", "secondary", and
"non-essential". The program 40 adjusts the measurement technique
based on this input value. For example, a boiler feed pump which is
"essential" to the operation of the powerhouse would be monitored
at shorter intervals than one which is "non-essential". In
addition, more measurement points and types of measurement points
would be created.
[0102] Since the user 48 selected "periodic vibration" in FIG. 4 as
the measurement technology, the user 48 is prompted in FIG. 7 to
specify various equipment parameters effecting vibration including
the equipment or component type 86, a reference driver speed 88 of
the equipment, speed units 90, a reference load 92 corresponding to
the reference operating speed 88, and an alarm criteria 94. Alarm
levels are adjusted automatically by the program 40 based on the
alarm criteria selection 94.
[0103] In FIG. 8, the user 48 is asked to provide setup information
required by the technologies selected in FIG. 4. This information
is used later during the detailed component setup.
[0104] A name for the equipment (Boiler feed Pump #1) has now been
created within the "powerhouse" area and is ready to go through a
more detailed configuration. The detailed configuration portion of
the program 40 is that portion of the program 40 which asks for
information that enables the measurement information to be set up
correctly. A detailed configuration of the boiler feed pump #1 is
being launched from the main program screen shown in FIG. 9. This
accomplished by selecting the file entitled "Boiler Feed Pump #1"
from the file tree structure shown in box 93 and then selecting the
"Configure" button 95.
[0105] In FIG. 10, the user 48 has defined a new configuration file
title "Boiler Feed Pump #1" shown at window 96. Configuration
information, including component types, couplings between
components, and physical characteristics of components, are stored
in the specified file. If desired, the user 48 may elect to save
configuration information in a user-defined warehouse file 98. A
read-only warehouse file is also provided as part of the program
40. At the screen shown in FIG. 10, the user 48 may select a
configuration which is already stored in one of the warehouse
files, thus avoiding the need to define a configuration when the
equipment (or like equipment) has been previously configured.
[0106] At the component design studio shown in FIG. 11, the user 48
is asked to identify components of the new configuration. Since the
boiler feed pump #1 includes a motor and a pump, each of the
components are selected from the list of rotating components and
displayed in the lower window 100 as an unconfigured motor 102 and
an unconfigured pump 104. A dashed line 106 connecting the motor
102 and pump 104 represents an unconfigured coupling. These
components may be configured for each technology that should be
applied to the specific equipment.
[0107] A typical procedure for configuring the components shown in
FIG. 11 will now be described. The motor 102 will be configured
first, then the pump 104, and last the coupling 106 between the
two.
[0108] General information relating to the motor is provided at the
screens shown in FIGS. 12 and 13. This information is needed both
for use in creating the data collection methods and also for the
programs that analyze the data.
[0109] Rotating equipment such as motors posses characteristics
that will generate specific vibration frequencies when the
equipment is running. The amplitude at these frequencies is used to
determine the condition of the equipment. During the detailed
configuration of the equipment, the program 40 asks the user 48 to
input this information at the screen shown in FIG. 14, if known.
For example, the user may input the number of phases, poles, rotor
bars and stator slots, if known, for a synchronous motor. The
inference engine 46 uses this information in part to select
appropriate analysis parameter information.
[0110] The user 48 is next taken to the screen shown in FIG. 15
where the user 48 is asked to select motor bearing types and thrust
load location, if applicable. Bearings are an important element
relating to the health of rotating equipment and are treated as
such by the program 40. Bearing information is important in the
setup of the measurement methods as well as the analysis of the
data. FIG. 16 shows a screen from which bearings may be looked up
from a list of known bearings, and FIG. 17 shows a screen that
enables the user 48 to look up bearings from master lists and
import them into the component definition.
[0111] In FIG. 18, the program 40 has processed the motor
information entered by the user 48 (including the selected
component type and the selected physical characteristics) and
determined the best or optimal measurement points 103 for the
motor. The best or optimal points chosen are those for which a
check mark appears in the column titled "Active?". A unique
measurement point identifier (PID) 105 is generated and associated
each with a measurement point 103. In an alternate embodiment of
the user interface screen shown in FIG. 18, an image of the motor
is obtained from stored equipment diagram files 62 (FIG. 2) and the
measurement points 103 are indicated on the motor image. In typical
conventional methods, the user 48 must possess substantial
knowledge of predictive maintenance practices in order to identify
the best measurement points 103. As seen in FIG. 18, the program 40
has identified these points for him.
[0112] In FIG. 19, the motor 102' is now configured for the
vibration technology selected back in FIG. 4. The user 48 now
chooses to configure the motor technology by selecting the motor
icon 108 at the bottom of the screen.
[0113] As seen in FIGS. 20 and 21, the motor technology requests
additional motor information from the user 48 (for example,
identification number, frame size, insulation class, rated RPM,
rated power, rated voltage, and rated current). The information
provided in FIG. 20 is used to determine measurement parameters for
collecting data while the information provided by the user 48 in
FIG. 21 is used for analysis of the data.
[0114] FIG. 22 shows measurement points that are selected for the
motor based on the information provided in FIGS. 20 and 21. The
measurement methods are also altered based on that information.
[0115] In FIG. 23, the user 48 may select icon 110 at the bottom of
the screen to configure the motor for infrared thermography data
collection. Thermography measurement points are set up in the
predictive maintenance database based on the information provided
at the screen shown in FIG. 24.
[0116] The user 48 selects icon 112 shown at the bottom of the
screen of FIG. 25 to configure the driven component (i.e., the
pump) for the vibration technology. This selection takes user 48 to
the screen shown in FIG. 26 which prompts the user 48 to provide
the requested information. Often, a user may not know all of the
configuration information that the program 40 requests. In such a
case, the user 48 simply needs to answer what is known and the
program 40 will create an optimal database from the information
that is available. Users who know more information about the
equipment details can enter this information into the screen shown
in FIG. 26 and by selecting the advanced screen button 114 located
in the lower left hand corner of the screen. The additional
information will be reflected in the setup of the actual predictive
maintenance database as it is built.
[0117] Additional information is requested in FIG. 27. Information
relating to the pump type 116, pump support 118, pumped fluid 120,
number of stages 121 and number of inlet/outlet impeller vanes 123,
is used by the program 40 to establish measurement parameters,
while information such as inlet type 122 and number of inlet/outlet
diffuser vanes 125 are used to determine specific analysis
frequencies.
[0118] Measurement points 124 (FIG. 28) are established by the
program 40 based on the information entered in FIGS. 26 and 27, and
the measurement points 124 are displayed on the screen shown in
FIG. 28. Each measurement point is identified by a unique
measurement point identifier (PID) 126. The measurement points are
used by the program 40 to establish the measurement parameters and
locations.
[0119] The user 48 is taken to the component design studio 44 (FIG.
2) in FIG. 29 where the coupling 106 between the configured motor
102' and the configured pump 104' is defined. The coupling 106
attaches the driver, in this case a motor 102', and a driven unit,
in this case a pump 104'. The type of coupling can effect the speed
of the driven unit.
[0120] In the program 40, the user 48 simply inputs the type of
coupling and any speed ratio information. Coupling types can be
found by expanding the menu selection under "Existing" shown at
124. This information enables the program 40 to calculate the
correct measurement point speeds. The speed at each measurement
point is an important piece of information used by the program 40
to establish analysis parameters which control how data is measured
and subsequently analyzed.
[0121] The "boiler feed pump #1" is now configured in FIG. 30,
including a configured coupling 106'. The user 48 is now ready to
instruct the program 40 to create a predictive maintenance database
for the configured equipment. The user 48 accomplishes this by
returning to the main program screen, shown in FIG. 31 (see also
FIG. 9), and clicking the hammer icon 128. As shown in FIG. 32,
pop-up progress screen 130 enables the user 48 to monitor the
progress of the program 40 as the database is created. A predictive
maintenance database file is created and can be viewed and edited
with a commercially available database program 54 (FIG. 2), such as
DBASE, as shown in FIGS. 33-41.
[0122] FIG. 33 provides a view of the main program screen from the
DBASE program. The screen indicates to the user 48 that two
equipment configurations ("Boiler Feed Pump #1" and "Condensate
Pump #1") are included within the PH01 Powerhouse area of the
database. From the screen shown in FIG. 33, the user 48 may edit,
move, delete, and generally manipulate data contained in the
database by use the appropriate buttons shown in the upper
right-hand portion of the screen. These buttons are generally
indicated at 182. To examine the measurement points output by the
program 40 for the Boiler Feed Pump #1, the user 48 selects "Boiler
Feed Pump #1" and then clicks the "OK" button 180 located in the
lower right-hand portion of the screen.
[0123] A list of measurement points created by the program 40 to
monitor the "Boiler Feed Pump #1" is shown in FIG. 34. Included in
the list are points for measuring vibrational displacement at the
outboard side of the motor 184, vibrational displacement at the
inboard side of the motor 186, motor leakage flux 188, temperature
190, and others. The list also includes points for visual
observations made by the user 48, including observation of motor
cleanliness 192 and observation of air flow through the motor
194.
[0124] FIGS. 35-41 illustrate various detailed information which is
included in the database for each measurement point. Some of the
information is based on data input to the program 40 by the user
48. As shown in FIG. 35, the user 48 is able to view and edit
measurement point information including a measurement point
identifier 192, measurement point description 194, measurement
units and code 196, motor rpm at the measurement point 198,
monitoring schedule 200, and number of values in statistical
calculations 202. The screen shown in FIG. 35 also shows that data
collection set information 204 (used during actual field data
collection) is included in the database. The data collection set
information includes an analysis parameter set 206 and an alarm
limit set 206.
[0125] Additional measurement point information shown in FIG. 36
includes probe type 204, sensor orientation 206, sensor position
208, sensitivity of the sensor 210, highest and lowest acceptable
signal levels 212, and other detailed information.
[0126] At the screen shown in FIG. 37, the user 48 is provided a
descriptive list of analysis parameters which are included in the
analysis parameter set 206 created by the program 40. Included in
the list is an AC motor rolling bearing analysis parameter 212, a
peakvue analysis parameter 214, an electric current-rotor bar
condition parameter 216, motor shaft current and voltage
measurement parameters 218, and others.
[0127] The analysis parameter sets control the manner in which data
is collected. These sets control two basic items. The first is time
waveform and frequency spectrum data collection along with any
special signal processing. The user 48 may view and edit spectrum
and waveform information contained in the database for each of the
analysis parameters listed in FIG. 37. For example, in FIG. 38 the
user 48 is able to view detailed information relating to waveform
and frequency data collection parameters for analysis parameter
number 1 entitled "AC MTR, ROLLING BRG (1600). Included in this
information is the spectral frequency setup 220, low frequency
signal conditioning limit 222 (for noise elimination), upper and
lower frequency limits 224, number of spectral lines to be acquired
226, number of averages 228, and type of analysis window 230.
[0128] The second item in the parameter analysis set is the
parameter bands (i.e., frequency ranges in which the amplitude is
measured) that will be measured and trended by the DBASE database
program. As shown in FIG. 39, the analysis parameter sets are
scalar values that correspond to waveform characteristics or
integrated energies over specified frequency ranges in the
spectrum. These may include the maximum peak to peak value 232 in
the waveform, crest factor 234, a narrow frequency interval around
on times turning speed (TS) 236, the amplitude of all frequencies
in a wide interval from 3.5 to 10.5.times.TS, and others. Each of
the parameter bands is displayed in FIG. 39 according to a
descriptive title, units type, type of parameter, and lower and
upper frequencies. Additionally, each parameter band is selected by
the program 40 based on information, including type of equipment
and component information, that was input to the program 40 by the
user 48.
[0129] As described above, the program 40 also creates alarm limit
sets which correspond to the analysis parameter sets. Alarm limit
sets typically vary between different types of equipment and are
generally dependent on expected amplitude levels. In FIG. 40, the
DBASE database program has displayed by descriptive title a list of
alarm limit parameters. Included in the list is an AC motor rolling
bearing (moderate) 242, Peakvue (moderate) 244, skin temperature
246, roller element bearing temperature 248, shaft current and
voltage alarms 250, and others.
[0130] Each alarm limit set contains amplitude limits which are
compared to the trended data in the analysis parameter bands. The
amplitude levels are based on the type of equipment input to the
program 40 by the user 48 and the unexpected operational
conditions. FIG. 41 shows the alarm limits for the twelve analysis
parameters given in FIG. 40. Included in FIG. 41 is a column
specifying for each alarm limit the units code 252, alarm type 254,
an alarm limit value indicative of a significant failure or fault
256, an alarm limit value indicative of a less severe failure or
alert 258, and other information.
[0131] As this example has shown, a user who knows very few
equipment details, or a user who knows all equipment details, can
use the program 40 to create a predictive maintenance database. The
program 40 creates a predictive maintenance database that is more
than adequate for data collection and analysis purposes, even when
few details are known about the equipment. The more information
that can be input into the program 40, the more detailed the
measurement points, analysis parameter sets, and alarm limit sets
will be.
[0132] Referring again to FIG. 30, the "boiler feed pump #1"
configuration may be reused to define other like configurations for
the creation of additional configurations in predictive maintenance
databases. In addition, each of the components 102', 104', 106' and
their corresponding templates may be reused in defining other
equipment configurations, as illustrated in Example 2 given
below.
EXAMPLE 2
[0133] In this example, FIGS. 42-52 will be used to illustrate the
ability of the program 40 to reuse already configured components
when configuring equipment.
[0134] As shown in box 140 of FIG. 42 (see also FIG. 10), the new
configuration created in this example will be called "condensate
pump #1". Completed configurations are listed in box 142.
[0135] At the screen shown in FIG. 43, the user 48 is prompted to
specify whether existing configuration information will be used.
Existing information can be recalled from one of three types of
stored information, including component information 144, template
information 146, and configuration information 148. Component
information includes the appropriate template information as well
as bearing setup information. Template information contains
detailed machine setup information (excluding bearing setup
information) and is stored in a component warehouse 64 (either a
read-only warehouse or a user-defined warehouse). Elements of a
component that are not changeable are considered as template
information. Configuration information includes complete equipment
train information, which usually consists of at least one driver
and one driven component connected by a coupling. The user 48
clicks the template 146 selection and is taken to the screen shown
in FIG. 44.
[0136] In FIG. 44, it can be seen that the template 146 may be
selected from a local file 153, a user-defined component warehouse
154, or a read-only component warehouse 155. The local file 153
includes templates that the user 48 will reuse only in the present
database. Typically, such templates are created for equipment that
can be found at only one site. The user warehouse 154 includes
templates which have been configured and stored by the user 48 and
can be edited, and the supplier warehouse 155 includes templates
provided by the program 40 supplier and cannot be edited. A list of
standard motor types contained in the supplier warehouse 155 are
displayed in box 152, and a motor 150 has been selected from the
list. Although there is template information present in the
component warehouse 155 for the selected motor 150, the motor 150
is considered to be unconfigured at this point since the physical
characteristics have not yet been defined.
[0137] General component definition information is provided by the
user 48 in FIG. 45 (see also FIG. 12). The motor component will be
created based on the information contained in the template
identified in box 156, which is grayed out and not editable since
this template is part of a predefined component warehouse that was
provided with the program 40. Information corresponding to motor
construction and bearing types is grayed out in FIGS. 46 and 47
(see also FIG. 14) as well. Thus, while the information can be
displayed, it is not changeable by the user 48.
[0138] Detailed bearing information is required, however, and this
information is defined by clicking the "Advanced" button 158 which
brings up the screen shown in FIG. 47. By clicking on next, the
bearing screen shown in FIG. 48 is displayed. The bearing
information shown in this screen has been input by the user 48.
[0139] After the above information has been provided, measurement
points 160 and corresponding identifiers 162 are created and
displayed as shown in FIG. 49. The measurement points 160 can be
edited by the user 48.
[0140] In FIG. 50, the motor 151' is now configured. The user 48
has selected a gearbox 164 from a list of predefined gearbox
components displayed in box 166. A popup box 168 prompts the user
48 as to whether the gearbox should be edited. Clicking "Yes" takes
the user 48 to the screen shown in FIG. 51. Since all information
relating to the gearbox has been predefined in the component
warehouse, the user 48 may only examine the information shown in
FIG. 51.
[0141] In FIG. 52, the "condensate pump #1" configuration has been
completed. The configuration includes the motor 150', gearbox 164',
a pump 170 which was selected from the local file 153 and not
edited, a coupling 172 interconnecting the pump 150' and gearbox
164', and a coupling 174 interconnecting the pump 170 and gearbox
164'. The user 48 is now ready to insert this information into his
predictive maintenance database as described above with reference
to FIGS. 31-33 (note that FIG. 32 includes the "condensate pump
#1") and to view or edit the new database information as described
in FIGS. 34-41.
[0142] As previously mentioned, there is no unique correct
knowledge base 52 or inference engine 46. Both must necessarily be
constructed according to a particular application. The knowledge
base 52 is a set of information and rules. The information is
physical data and the rules relate different types of physical data
one to the other. For example, a rule could relate a particular
motor type of a particular physical description to a set of
measurement points. The knowledge base 52 does not directly relate
anything to a predictive maintenance database.
[0143] The inference engine 46 relates data in the knowledge base
52 and data generated by the knowledge base and its rules,
collectively or individually, to a particular set of data in a
predictive maintenance database. Since these concepts are somewhat
complicated, they may best be understood by reference to a
simplified example of a knowledge base 52 and inference engine 46
as given below in Example 3.
EXAMPLE 3
[0144] In this example, information for a predictive maintenance
database will be created for an AC induction motor. The following
information was known about the motor and was input to the program
40 to configure the motor:
[0145] Number of Phases: 3
[0146] Number of Poles: 2
[0147] Number of Rotor Bars: 68
[0148] Number of Stator Slots: 54
[0149] Inboard Bearing: antifriction
[0150] Outboard Bearing: antifriction
[0151] Shaft RPM: 3575
[0152] Probe Type: casing
[0153] Line Frequency: 60 Hz
[0154] Based on this information, the following steps illustrate
how the analysis parameter and alarm limit sets were created by the
program 40. It is worth noting that it is common for a user 48 to
not know all of the information given above. As previously
described, minimal information is needed for the program 40 to
create the analysis parameter sets and alarm limit sets. For
rotating equipment such as AC induction motors, the absolute
minimum information needed is the specification of an equipment
type and the speed of the equipment.
[0155] Analysis Parameter Set Creation
[0156] Step 1: The first rule for an AC induction motor is to
create the analysis parameter (AP) set and alarm limit (AL) set
based on bearing type and name the sets as follows:
[0157] Create AP Set "AC MTR,ROLLING BRG"
[0158] Create AL Set "AC MTR,ROLLING BRT"
[0159] Step 2: Next, following rules for AC motors, the inference
engine 46 will in the measurement details of the analysis parameter
set as follows:
[0160] Set the Maximum Analysis Frequency=70.5 Orders
[0161] Set the Minimum Analysis Frequency=0.0 Orders
[0162] Set the Lines of Resolution=1600 lines
[0163] Set Number of Averages=5
[0164] Set Spectral Averaging Mode=Normal Mode
[0165] Set Window Type=Hanning
[0166] Set Spectral Weighting=None
[0167] Set Perform 1/3Octave Analysis=False
[0168] No special signal processing parameters will be used.
[0169] A special time waveform will be collected in acceleration
units with the maximum time in the waveform based on a spectral
Fmax of 80 orders with 1024 points in the waveform.
[0170] The number of lines of resolution selected was based on the
following rules:
[0171] IF 3000<Turning Speed, THEN
[0172] Set # Lines=1600
[0173] IF 800<Turning Speed.ltoreq.3000, THEN
[0174] Set # Lines=800
[0175] IF Turning Speed.ltoreq.800, THEN
[0176] Set # Lines=400
[0177] Since the Turning Speed=3575 was greater than 3000, the
number of lines set= 1600.
[0178] Step 3: The remaining details of the parameter set forth
above are default settings for this type of AC induction motor. For
this type motor, the rules provide that the following parameter
bands will be measured for trending:
[0179] Peak to Peak Waveform band (Overall Amplitude)
[0180] Crest Factor Waveform band (Overall Amplitude)
[0181] Subharmonic Frequency band (0.0 to 0.8 orders)
[0182] 1.times.Turning Speed Frequency band (0.8 to 1.4 orders)
[0183] 2.times.Turning Speed Frequency band (1.4 to 2.4 orders)
[0184] 3.times.to 8.times.Turning Speed Frequency band (2.4 to 8.4
orders)
[0185] 9.times.to 25.times.Turning Speed Frequency band (8.4 to
25.4 orders)
[0186] 25.times.to 75.times.Turning Speed Frequency band (24.4 to
75.4 orders)
[0187] 1 kHz to 20 kHz High Frequency band (1000 to 20,000 Hz)
[0188] Step 4: The following parameters bands will be created based
on certain rules:
[0189] 2.times.Line Frequency band (115 to 125 Hz)
[0190] The creation of this band is dependent on the Line Frequency
and the following rule:
[0191] SET LF=Line Frequency, THEN
[0192] SET HZ AP Band to Monitor: High freq=(2*LF)+5, Low
Freq=(2*LF)-5
[0193] The default value will be 60 Hz, which is the line frequency
standard in North America.
[0194] Based on the above rule, the parameter band for 2.times.Line
Frequency band will trend the frequency range between 115 Hz to 125
Hz.
[0195] Step 5: Next, the inference engine 46 creates a parameter
band to trend the frequency related to the number of rotor bars and
a parameter band to trend the frequency related to the number of
stator slots.
[0196] Rotor Bar Pass Frequency band (63.0 to 73.0 orders)
[0197] Stator Slot Frequency band (49.0 to 59.0 orders)
[0198] These frequency ranges were determined based on the
following rule:
[0199] IF Number of Stator Slots and Rotor Bars is known,
[0200] ANDIF Number of Stator Slots>21
[0201] AND Number of Stator Slots is divisible by 3, THEN
[0202] Assume # of Rotor Bars and Stator Slots is correct
[0203] ELSE Assume Stator and Rotor Bars is incorrect and use 0.0
for each.
[0204] Rotor Bars pass freq range=(#rotor bars-5) to (#rotor
bars+5) orders
[0205] Sator Slot pass freq range=(#stator slots-5) to (#stator
slots+5) orders
[0206] Based on the number of rotor bars=68 and the number of
stator slots=54 and the above rule, the Rotor Bar Pass Frequency
band will trend the amplitude of the frequencies between 63.0 and
73.0 orders while the Stator Slot Frequency band will trend the
amplitude of the frequencies between 49.0 to 59.0 orders.
[0207] Alarm Limit Set Creation
[0208] The alarm limit set created by the inference engine 46 will
include alarm limits appropriate for each of the parameter bands
described above. The alarm limit values are based on the type of
equipment and the alarm criteria specified by the user 48. For this
example, the alarm criteria was set to moderate. If the alarm
criteria had been set to "smooth", the default alarm levels would
be set at alarm limits which are lower than those for the
"moderate" setting. Conversely, if the alarm criteria had been
specified as "rough", the default alarm levels would be set at
alarm limits higher than those for the "moderate" setting.
[0209] Measurement Points
[0210] FIG. 53 is a table of candidate measurement points which the
program 40 uses as rules to determine, based on the operational
significance defined by the user 48, which measurement points will
be generated for an AC induction motor. The values of 1-5 are used
in FIG. 53 to indicate operational significance where "critical"
corresponds to an operational significance value of "1",
"essential" corresponds to "2", "important" corresponds to "3",
"secondary" corresponds to "4", and "non-essential" corresponds to
"5". In this example, the operational significance of the motor was
set as "essential" which equates to an operational significance of
"2" in the table shown in FIG. 53. Accordingly, each candidate
measurement point for which a check mark appears in column 2 was
selected as a measurement point for the motor.
[0211] In summary, the above examples and description explain how
the analysis parameter sets, alarm limit sets, and measurement
points are selected by the program 40 based on information provided
to the program 40 by the user 48. The selections are made based on
rules known by the program's inference engine 46. The inference
engine rules are preferably accepted industry practices which are
likely to be unknown to a novice user of the program 40. The rules
are designed to produce a predictive maintenance database which
will allow the user 48 to collect predictive maintenance data more
correctly for each type of equipment that is to be monitored, even
when minimal information is known about each piece of equipment,
thereby enabling the user 48 to achieve a greater degree of
analysis accuracy with significantly less information, experience,
and time.
[0212] It will be appreciated, however, that the rules are not
invariable. For example, in some applications it may be unnecessary
or undesirable to determine operational significance, and in such
case, there would be no need for rules related to operational
significance. Thus, the rules can and should be changed for
different circumstances.
[0213] Lookup Technique
[0214] While it is preferred to use an inference engine 46 and
knowledge base 52 as described above, the invention may be
implemented with a more simplistic lookup technique or combination
of both the lookup technique and the inference engine technique. In
the latter case, the user can choose between the two
techniques.
[0215] As shown in FIG. 53, the lookup technique is selected from
the pull-down menu labeled "Options" 218. The lookup technique is
preferably implemented by providing a Master Component/Database
file 200 that contains at least two fields of data, namely, a
component identification field 202 and a database information field
210, each corresponding to a particular component, such as a motor
or a pump. While it would be possible to use a single component
identification field and a single database information field, in
most applications these two "fields" will actually be a group of
related fields. For example, the component identification field may
include a separate field for the manufacturer's name 204, the model
number 206, and each physical attribute 208 such as the horsepower
rating of a motor or the number of rotor bars in a motor. Likewise,
the database information field will normally include many different
fields such as those disclosed in FIGS. 34-41.
[0216] To build a database using the lookup technique, the user 48
first uses the component identification field 202 to find and
select a record in the master component/database file 200
corresponding to the component to be monitored. When the component
record is selected, either automatically or by the user 48, the
database information (or record) found in the master
component/database file 200 is added to the database that is being
built.
[0217] As mentioned above, the component identification field 202
may contain a number of fields containing physical attributes or
physical descriptions of the component or a manufacturer and model
number, and it preferably has both. To select a component, the user
48 first names the component in the same manner as discussed above
with reference to FIG. 6. The user 48 may also provide an
operational significance and alarm information as discussed above,
but this is not required.
[0218] After naming the component, a corresponding component record
may be found in the master component/database file 200 by two basic
techniques. In one technique, the user 48 provides physical
criteria describing the physical component. For example, the user
may provide the following physical criteria of a component such as
a motor: 3 phases, 2 poles, 68 rotor bars, 54 stator slots, rated
RPM of 3575, rated horsepower of 25, rated voltage of 440 volts and
rated current of 200 amps. When the user 48 is finished entering
the physical criteria, he initiates a search, such as by clicking
on a search button 212, 214, and the program 40 searches the
physical attributes in the component identification field 202 for
the best fit. The program 40 may automatically select the best fit
component record from the master component/database file 200 and
automatically add the selected record to the database being built.
However, preferably, the program 40 may also display a best fit
group of component records and allow the user 48 to select one
record. The best fit group would usually include all of the
component records that exactly fit the physical criteria provided
by the user 48, plus additional records that had physical
attributes that were near the physical criteria provided by the
user 48.
[0219] The best fit group of records is prioritized with the most
likely candidate record listed earlier or higher in the best fit
group. Priority may be based on the closeness of the fit and/or the
prevalence or popularity of the component in the market. For
example, a component priority could be proportional to the quantity
of the component manufactured or the quantity of the components in
use in a particular market.
[0220] When the user 48 selects one record from the best fit group,
such as by clicking on it, that selected record is associated with
the name of the component previously provided by the user 48, and
the combined information is added to the database being created as
another record. In this lookup technique, the database information
is contained in the master component/database file 200 and there is
no need to perform any operations on the information in the master
component/database file 200 in order to generate database
information for the database being created.
[0221] The other way of selecting a component record from the
master component/database file 200 is by using the manufacturer and
model number. In this case, the user 48 inputs the model number,
which preferably includes the manufacturer as shown in block 216 of
FIG. 53 (i.e., "GE" represents General Electric as the
manufacturer), and the program 40 searches the identification field
for a record having the exact model number. If an exact match is
found, it is either automatically chosen and the database being
created is updated with the chosen record as described above, or
the record having the exact match is displayed. Whether the
selection is made automatically or manually is preferably
determined by a user option.
[0222] If more than one exact match is found, all of the exact
matches are displayed and the user is requested to select one. If
no exact matches are found, a best fit group is displayed in the
manner described before. The group is prioritized in an order based
upon the closeness of the model number in a record as compared to
the model number provided by the user and based upon the popularity
or prevalence of each component in the group.
[0223] The searching capabilities described above provide
significant advantages over known predictive database programs.
While it was certainly possible to copy portions of databases
produced by prior art programs and attach such copies to another
database, there was no specialized searching capability for finding
what needed to be copied. To the extent that one database was used
to make another, it was generally a manual process. In the lookup
technique described herein, the searching capability enables the
user to build a database systematically using some information
about the machine or machines for which the database is being
built. The searching feature is flexible in that a variety of
information may be used to conduct the search, including physical
information, model numbers or manufacturers. In addition the
program provides for building a configuration composed of more than
one machine and using the lookup technique for building a database
for the overall configuration. In such case the lookup technique
searches the master component/database file 200 first for the
specific combination of machines, such as a particular pump,
coupling and pump. If the specific combination is found, it is used
to construct the database. If the combination is not found, the
master component/database file 200 is searched for the individual
machines seperately and the database information for each machine
is combined to produce the database for the overall
configuration.
[0224] It will be appreciated that the lookup technique requires
the use of a master component/database file 200 that is typically
much larger than any file necessary when the inference engine
technique is used. To facilitate operations, the master
component/database file 200 may actually exist as a number of
files. For example, a separate file may be created for different
types of components. A separate file may be provided for pumps,
gear boxes, motors, etc. Also, separate files may be provided for
different sub-categories of components, such as induction motors
versus synchronous motors. Also, conventional searching techniques
and enhancements would be used for searching and finding data
within the files. Preferably, the data in all of the fields would
be indexed to enhance searching.
[0225] If alarm information and/or operational significance
information is to be included in the lookup technique, the database
generated by the technique must reflect the operational
significance and/or the alarm level provided by the user 48. One
way to incorporate operational significance into the lookup
technique is to provide multiple entries (records) for the same
component, with a separate record for each operational
significance. In other words, if there were three levels of
operational significance that a user 48 could choose, each
component in the master component/database file 200 would require
three separate records, one for each operational significance.
Likewise, if alarm levels are used, each component must have a
separate record for each alarm level. If both operational
significance and alarm level is provided, the master
component/database file 200 should have a separate record for each
combination of operational significance and alarm level. For
example, a first record must be provided for alarm level one,
operational significance one, and a second record must be provided
for alarm level one, operational significance two. It will be
appreciated that the use of alarm levels and operational
significance can cause the master component/database file 200 to
grow dramatically.
[0226] To avoid this type of growth in the size of the files, one
may blend the inference engine technique and the lookup table
technique. For example, the lookup technique could be used to
produce preliminary database information based solely on the
component identity ignoring alarm level and operational
significance. Then, an inference engine 46 or a set of rules can be
used to modify the preliminary database information based upon both
the alarm levels and the operational significance provided by the
user 48.
[0227] It is contemplated, and will be apparent to those skilled in
the art from the foregoing specification, drawings, and examples
that modifications and/or changes may be made in the embodiments of
the invention. Accordingly, it is expressly intended that the
foregoing are only illustrative of preferred embodiments and modes
of operation, not limiting thereto, and that the true spirit and
scope of the present invention be determined by reference to the
appended claims.
* * * * *