U.S. patent application number 12/795561 was filed with the patent office on 2011-03-03 for component adaptive life management.
This patent application is currently assigned to Jentek Sensors, Inc.. Invention is credited to Neil J. Goldfine, David C. Grundy, David A. Jablonski, Robert J. Lyons, Yanko K. Sheiretov, Floyd W. Spencer, Andrew P. Washabaugh, Vladimir A. Ziberstein.
Application Number | 20110054806 12/795561 |
Document ID | / |
Family ID | 43626104 |
Filed Date | 2011-03-03 |
United States Patent
Application |
20110054806 |
Kind Code |
A1 |
Goldfine; Neil J. ; et
al. |
March 3, 2011 |
Component Adaptive Life Management
Abstract
A framework for adaptively managing the life of components. A
sensor provides non-destructive test data obtained from inspecting
a component. The inspection data may be filtered using reference
signatures and by subtracting a baseline. The filtered inspection
data and other inspection data for the component is analyzed to
locate flaws and estimate the current condition of the component.
The current condition may then be used to predict the component's
condition at a future time or to predict a future time at which the
component's condition will have deteriorated to a certain level. A
current condition may be input to a precomputed database to look up
the future condition or time. The future condition or time is
described by a probability distribution which may be used to assess
the risk of component failure. The assessed risk may be used to
determine whether the part should continue in service, be replaced
or repaired. A hyperlattice database is used with a rapid searching
method to estimate at least one material condition and one usage
parameter, such as stress level for the component. The hyperlattice
is also used to rapidly predict future condition, associated
uncertainty and risk of failure.
Inventors: |
Goldfine; Neil J.; (Newton,
MA) ; Sheiretov; Yanko K.; (Waltham, MA) ;
Washabaugh; Andrew P.; (Chula Vista, CA) ;
Ziberstein; Vladimir A.; (Chestnut Hill, MA) ;
Grundy; David C.; (Chelmsford, MA) ; Lyons; Robert
J.; (Boston, MA) ; Jablonski; David A.;
(Whitman, MA) ; Spencer; Floyd W.; (Albuquerque,
NM) |
Assignee: |
Jentek Sensors, Inc.
Waltham
MA
|
Family ID: |
43626104 |
Appl. No.: |
12/795561 |
Filed: |
June 7, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61184672 |
Jun 5, 2009 |
|
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|
Current U.S.
Class: |
702/34 |
Current CPC
Class: |
G07C 3/00 20130101; G01N
2203/0212 20130101 |
Class at
Publication: |
702/34 |
International
Class: |
G01B 3/44 20060101
G01B003/44 |
Goverment Interests
GOVERNMENT SUPPORT
[0002] The invention was supported, in whole or in part, by a grant
NNX09CE84P from NASA and by a grant N68335-08-C-0008 from the U.S.
Navy. The Government has certain rights in the invention.
Claims
1. A computer-readable storage medium comprising
computer-executable instructions that, when executed by at least
one processor, perform a method comprising acts of: receiving at
least two sets of sensor data, each of the at least two sets of
sensor data comprising spatial data for a measured material
condition of a component; spatially registering the at least two
sets of sensor data with respect to each other and the component;
computing a change in the material condition of the component from
the spatially registered at least two sets of sensor data;
estimating the current condition based at least in part on the
change in the material condition; and predicting a future condition
of the component at a future time based at least in part on the
estimated current condition, the future condition of the component
being predicted using a database comprising a plurality of
precomputed material conditions of the component, each precomputed
material condition computed for a respective operating condition
and time; the method further comprising generating the precomputed
material conditions using a flaw growth model; storing the
precomputed material conditions in the database; filtering at least
one of the at least two sets of sensor data with at least one flaw
signature; and estimating a current condition of the component from
the filtered sensor data, and a stored database of historical
sensor data from a simple element selected to represent the
component material and flaw growth behavior.
2. The computer-readable storage medium of claim 1, wherein each of
a plurality of data points in the database are generated for a
respective combination of an equivalent number of fatigue cycles
and stress level.
3. The computer-readable storage medium of claim 1, wherein the
stored database comprises at least one coupon test representative
of the component.
4. The computer-readable storage medium of claim 1, wherein
predicting the future condition of the component comprises
estimating a service loading experienced by the component based at
least in part on the change.
5. The computer-readable storage medium of claim 1, wherein the act
of estimating the current condition comprises using the change in
material properties and archived data, the archived data relating
flaw size to sensor signal data.
6. A method of predicting a risk of failure before a future time,
the method comprising: inspecting a feature of a component using a
non-destructive testing (NDT) method, wherein the NDT method is
performed at a plurality of inspection times at a plurality of
locations on the component, the NDT method producing inspection
data for the plurality of locations at each of the plurality of
inspection times; storing the inspection data in a
computer-readable storage medium; operating at least one processor
to determine, based at least in part on the inspection data, if a
damage feature is growing within the component and, when
insufficient information exists to reliably detect the damage
feature using the inspection data at one of the inspection times,
generating an enhanced response from the inspection data at two or
more of the inspection times and using a precomputed database with
two or more dimensions as a function of sensed condition and usage
that is searched to determine the risk of failure before the future
time.
7. The method of claim 6, wherein the enhanced response is a
function derived from sensor data at a same location on the
component for each of the two or more inspection times.
8. The method of claim 7, wherein data at other locations is also
used to formulate the function to generate enhanced response.
9. The method of claim 8, wherein a signature library is used to
derive the enhanced response.
10. The method of claim 6, wherein the NDT method is an eddy
current array.
11. The method of claim 6, wherein the plurality of locations
comprise a fatigue critical location on the component, and usage is
measured in equivalent fatigue cycles, and the database is
generated using a damage evolution model.
12. The method of claim 6, wherein: the plurality of inspection
times comprises a first and second time, the first time being a
time before the component is put into service and the second time
being a time after the component is put in service, and the
inspection data comprises first inspection data taken at the first
time and second inspection data taken at the second time.
13. The method of claim 6, wherein the flaw is a crack in the
component and it is determined that insufficient information exists
to reliably detect the crack using the inspection data at one of
the plurality of inspection times if a probability of detecting the
crack is below a threshold.
14. The method of claim 13, wherein the threshold is about 90
percent probability of detection, and failure comprises a size of
the crack reaching a critical crack size.
15. A method of predicting a future time at which a critical damage
level will be reached, the method comprising: inspecting a feature
of a component using a non-destructive testing (NDT) method,
wherein the NDT method is performed at a plurality of inspection
times at a plurality of locations of the component, the NDT method
producing inspection data for the plurality of locations at each of
the plurality of inspection times; storing the inspection data in a
computer-readable storage medium; operating at least one processor
to determine, based at least in part on the inspection data, if a
damage feature is growing within the component and, when
insufficient information exists to reliably detect the damage
feature using the inspection data at one of the plurality of
inspection times, to generate an enhanced response from the
inspection data at two or more of the plurality of inspection times
and using a database generated using a model of damage evolution to
predict the probability distribution of future times at which the
critical damage level will be reached.
16. The method of claim 15, wherein the future time is measured in
equivalent fatigue cycles, the damage level is crack size, the
critical damage level is a critical crack size, and the location of
interest on the component is a fatigue critical location.
17. A method for tracking process progression comprising: operating
a processor to: process a first inspection image of a component to
rank an original plurality of locations of the component, wherein
the original plurality of locations are ranked based on a
quantitative measure that correlates with predicted crack growth
rate at the respective location; filter a second inspection image
of the component using filters devised from signatures to suppress
indications that are not representative of a condition of interest
and enhance the response of conditions that are more likely to
represent the condition of interest; and re-rank the original
plurality of locations using the filtered second inspection image
including additional highly ranked locations, wherein the second
inspection image is acquired at a later time than the first
inspection image.
18. The method of claim 17, further comprising: extracting the
signatures from a representative fatigue test article; and storing
the signatures in a signature library on a computer-readable
storage medium.
19. The method of claim 17, wherein the quantitative measure is a
peak conductivity at the respective location.
20. The method of claim 17, wherein the quantitative measure is a
half-height width of conductivity dip at the respective
location.
21. The method of claim 17, wherein the quantitative measure is a
peak filter response at the respective location.
22. The method of claim 17, wherein a fatigue progression track is
identified based on a statistically significant trend in
non-destructive test (NDT) data from two or more inspection times,
where the top ranked location is maintained for each inspection
until the end of the component's useful life.
23. The method of claim 22, wherein multiple NDT passes are
recorded at each inspection time.
24. A computer-readable storage medium comprising
computer-executable instructions that, when executed by at least
one processor, perform a method comprising acts of: spatially
registering a baseline response of a component and a current
response of the component; after the spatially registering,
subtracting the baseline response from the current response; and
estimating a probability distribution of a current condition of the
component by applying a statistical analysis using a set of
responses from one or more simplified elements.
25. The computer-readable storage medium of claim 24, wherein: the
baseline response is a digital non-destructive test (NDT) image
from an inspection performed prior to deployment of the component,
the current response is a digital NDT image from an inspection
performed after deployment and during a service life of the
component.
26. The computer-readable storage medium of claim 24, wherein
estimating the probability distribution of the current condition of
the component comprises estimating a probability density function.
Description
RELATED APPLICATION
[0001] This application claims the benefit of U.S. Provisional
Application No. 61/184,672, filed on Jun. 5, 2009, the entire
teachings of which application are incorporated herein by
reference.
BACKGROUND OF THE INVENTION
[0003] There are many applications where using a component to
failure is unacceptable, and thus the component must be replaced
when the risk of failure is too high. The decision of when to
retire a component is a tradeoff between at least the cost of
replacement and the risk of failure should the part continue to be
used.
[0004] Component failure is preceded by deterioration in the
condition of the component. Deterioration of a component's
condition is caused by the development and growth of flaws in the
component. Flaws for metals may include cracks, microcracks,
inclusions, residual stress variations, microstructure variations,
mechanical damage such as dents and scratches, corrosion pits, and
machining effects. Flaws for composites might include fiber damage,
bridging, impact damage, disbands, and delaminations. Flaws may
originate during manufacture or develop once the component is in
service. While in service the component may be exposed to operating
conditions that lead to the development and/or further growth of
the flaw. Different types of components may be more sensitive to
different types of loads. Operating conditions that may affect the
condition of a component may include temperature, temperature
variation (e.g., freeze-thaw cycles), acceleration, vibration,
voltage, pressure, rotational speed, mechanical stress, static
loading, dynamic loading, impact events, and any other physical
process that contributes to the development and/or growth of
component flaws.
[0005] In many applications a component is in use intermittently
and thus the operating conditions may not be persistent in time.
Accordingly, the in-service time of a component may be measured in
effective usage cycles, rather than in time directly. For example,
an airplane component may be exposed to adverse operating
conditions principally during each take off and landing cycles (or
ground-air-ground, "GAG", cycles). The operating environment while
the aircraft is grounded or cruising may have significantly less
contribution to flaw growth than the operating conditions during
takeoff and landing. Accordingly, a suitable in-service time unit
may be takeoff/land cycles. Though, other suitable measures of
in-service time may be used.
[0006] Safe life models have been used to predict the life of
components. These models consider the operating conditions that
cause damage to a component and estimate the intensity of these
conditions while the component is in service. Assuming an initial
flaw site, safe life models predict the growth of the flaw as the
component is exposed to worst case operating conditions. Component
failure may be defined, for example, by a point in the growth of a
flaw in the component at which the component may no longer serve
its intended purpose. The component may be replaced when the
service time of the component reaches some fraction of the service
time at which the component is predicted by the safe life models to
fail (e.g. 50%).
[0007] Periodic inspection of components may also be used to detect
flaws. The inspection may not only look for the presence of flaws
but also to characterize the flaw with one or more features. For
example, a crack in a component may be characterized by the crack's
length. Component flaw growth models may then be used to predict,
for example, the likelihood the flaw will lead to component failure
by a future time. Plot 100, shown in FIG. 1, sketches a curve 101
representing the probability of failure within a time, .DELTA.t. A
damage tolerance limit 103 is selected based on an acceptable
probability of failure 105.
[0008] Because failure is probabilistic, inspections are
traditionally scheduled periodically so that a flaw can be detected
early in its growth cycle, well before it is likely to develop to
the point of causing component failure. Different inspection
technologies will be capable of detecting flaws at different points
in their growth cycle and therefore the inspection interval depends
upon the type of inspection being performed and its expected
detection performance at the location of interest.
SUMMARY OF THE INVENTION
[0009] In accordance with an embodiment of the invention, there is
provided a computer-readable storage medium comprising
computer-executable instructions that, when executed by at least
one processor, perform a method comprising acts of: receiving at
least two sets of sensor data, each of the at least two sets of
sensor data comprising spatial data for a measured material
condition of a component; spatially registering the at least two
sets of sensor data with respect to each other and the component;
computing a change in the material condition of the component from
the spatially registered at least two sets of sensor data;
estimating the current condition based at least in part on the
change in the material condition; and predicting a future condition
of the component at a future time based at least in part on the
estimated current condition, the future condition of the component
being predicted using a database comprising a plurality of
precomputed material conditions of the component, each precomputed
material condition computed for a respective operating condition
and time. The method further comprises generating the precomputed
material conditions using a flaw growth model; storing the
precomputed material conditions in the database; filtering at least
one of the at least two sets of sensor data with at least one flaw
signature; and estimating a current condition of the component from
the filtered sensor data, and a stored database of historical
sensor data from a simple element selected to represent the
component material and flaw growth behavior.
[0010] In further, related embodiments, each of a plurality of data
points in the database may be generated for a respective
combination of an equivalent number of fatigue cycles and stress
level. The stored database may comprise at least one coupon test
representative of the component. Predicting the future condition of
the component may comprise estimating a service loading experienced
by the component based at least in part on the change. The act of
estimating the current condition may comprise using the change in
material properties and archived data, the archived data relating
flaw size to sensor signal data.
[0011] In another embodiment according to the invention, there is
provided a method of predicting a risk of failure before a future
time. The method comprises inspecting a feature of a component
using a non-destructive testing (NDT) method, wherein the NDT
method is performed at a plurality of inspection times at a
plurality of locations on the component, the NDT method producing
inspection data for the plurality of locations at each of the
plurality of inspection times; storing the inspection data in a
computer-readable storage medium; operating at least one processor
to determine, based at least in part on the inspection data, if a
damage feature is growing within the component and, when
insufficient information exists to reliably detect the damage
feature using the inspection data at one of the inspection times,
generating an enhanced response from the inspection data at two or
more of the inspection times and using a precomputed database with
two or more dimensions as a function of sensed condition and usage
that is searched to determine the risk of failure before the future
time.
[0012] In further, related embodiments, the enhanced response may
be a function derived from sensor data at a same location on the
component for each of the two or more inspection times. Data at
other locations may also be used to formulate the function to
generate enhanced response. A signature library may be used to
derive the enhanced response. The NDT method may be an eddy current
array. The plurality of locations may comprise a fatigue critical
location on the component, and usage may be measured in equivalent
fatigue cycles, and the database may be generated using a damage
evolution model. The plurality of inspection times may comprise a
first and second time, the first time being a time before the
component is put into service and the second time being a time
after the component is put in service, and the inspection data may
comprise first inspection data taken at the first time and second
inspection data taken at the second time. The flaw may be a crack
in the component and it may be determined that insufficient
information exists to reliably detect the crack using the
inspection data at one of the plurality of inspection times if a
probability of detecting the crack is below a threshold. The
threshold may be about 90 percent probability of detection, and
failure may comprise a size of the crack reaching a critical crack
size.
[0013] In another embodiment according to the invention, there is
provided a method of predicting a future time at which a critical
damage level will be reached. The method comprises inspecting a
feature of a component using a non-destructive testing (NDT)
method, wherein the NDT method is performed at a plurality of
inspection times at a plurality of locations of the component, the
NDT method producing inspection data for the plurality of locations
at each of the plurality of inspection times; storing the
inspection data in a computer-readable storage medium; operating at
least one processor to determine, based at least in part on the
inspection data, if a damage feature is growing within the
component and, when insufficient information exists to reliably
detect the damage feature using the inspection data at one of the
plurality of inspection times, to generate an enhanced response
from the inspection data at two or more of the plurality of
inspection times and using a database generated using a model of
damage evolution to predict the probability distribution of future
times at which the critical damage level will be reached.
[0014] In further, related embodiments, the future time may be
measured in equivalent fatigue cycles, the damage level may be
crack size, the critical damage level may be a critical crack size,
and the location of interest on the component may be a fatigue
critical location.
[0015] In another embodiment according to the invention, there is
provided a method for tracking process progression comprising
operating a processor to: process a first inspection image of a
component to rank an original plurality of locations of the
component, wherein the original plurality of locations are ranked
based on a quantitative measure that correlates with predicted
crack growth rate at the respective location; filter a second
inspection image of the component using filters devised from
signatures to suppress indications that are not representative of a
condition of interest and enhance the response of conditions that
are more likely to represent the condition of interest; and re-rank
the original plurality of locations using the filtered second
inspection image including additional highly ranked locations,
wherein the second inspection image is acquired at a later time
than the first inspection image.
[0016] In further, related embodiments, the method may further
comprise extracting the signatures from a representative fatigue
test article; and storing the signatures in a signature library on
a computer-readable storage medium. The quantitative measure may be
a peak conductivity at the respective location. The quantitative
measure may be a half-height width of conductivity dip at the
respective location. The quantitative measure may be a peak filter
response at the respective location. A fatigue progression track
may be identified based on a statistically significant trend in
non-destructive test (NDT) data from two or more inspection times,
where the top ranked location is maintained for each inspection
until the end of the component's useful life. Multiple NDT passes
may be recorded at each inspection time.
[0017] In another embodiment according to the invention, there is
provided a computer-readable storage medium comprising
computer-executable instructions that, when executed by at least
one processor, perform a method comprising acts of: spatially
registering a baseline response of a component and a current
response of the component; after the spatially registering,
subtracting the baseline response from the current response; and
estimating a probability distribution of a current condition of the
component by applying a statistical analysis using a set of
responses from one or more simplified elements.
[0018] In further related embodiments, the baseline response may be
a digital non-destructive test (NDT) image from an inspection
performed prior to deployment of the component, and the current
response may be a digital NDT image from an inspection performed
after deployment and during a service life of the component.
Estimating the probability distribution of the current condition of
the component may comprise estimating a probability density
function.
[0019] The foregoing is a non-limiting summary of the invention,
which is defined by the attached claims.
BRIEF DESCRIPTION OF DRAWINGS
[0020] The accompanying drawings are not intended to be drawn to
scale. In the drawings, each identical or nearly identical
component that is illustrated in various figures is represented by
a like numeral. For purposes of clarity, not every component may be
labeled in every drawing. In the drawings:
[0021] FIG. 1 is a plot sketching the probability of failure within
a time .DELTA.t given a current damage level of a component;
[0022] FIG. 2 is a block diagram of an inspection system according
to some embodiments;
[0023] FIG. 3 is a block diagram of a system for distributing
hyperlattices to inspection systems according to some
embodiments;
[0024] FIG. 4A is a plot illustrating at least a portion of a
hyperlattice according to some embodiments;
[0025] FIG. 4B is a plot showing predicting flaw growth in a
component under different operating conditions;
[0026] FIGS. 5A-5B is a flow diagram of a method for adaptively
managing the life of a component according to some embodiments;
[0027] FIG. 6A is an example presentation of inspection data
according to some embodiments;
[0028] FIG. 6B is a example presentation of indications identified
from inspection data according to some embodiments;
[0029] FIG. 6C is an example user interface for receiving input
from an operator that indicates further actions to be performed for
a set of indications;
[0030] FIG. 6D is a sketch of a component with markers for
facilitating spatial registration of multiple sets of inspection
data imaged from the component;
[0031] FIG. 6E is an illustration of an alignment process for two
sets of inspection data that utilizes the images of markers on the
inspected component;
[0032] FIG. 7A is a data plot of conductivity data obtained from a
titanium alloy sample after 15,000 and 21,000 stress cycles;
[0033] FIG. 7B is a data plot of change in conductivity after
baselining the 21,000 stress cycle data with the 15,000 stress
cycle data;
[0034] FIG. 8A is a sketch illustrating metrics for characterizing
a flaw detection;
[0035] FIG. 8B is an example plot of the measured response to a
flaw (a) versus the actual size of the flaw (a);
[0036] FIG. 9A shows a set of plots illustrating probability
distributions for inputs into a hyperlattice;
[0037] FIG. 9B is a block diagram illustrating operation of a
prediction module according to some embodiments;
[0038] FIG. 9C is an example cumulative distribution function
output from a prediction module;
[0039] FIG. 10A is a sketch of a representation of a portion of a
hyperlattice space according to some embodiments;
[0040] FIG. 10B is a sketch illustrating a representation of the
risk of failure according to some embodiments; and
[0041] FIG. 11 is a flow diagram of a method for adaptively
managing the life of a component according to some embodiments.
[0042] FIG. 12A-12Z show a graphical user interface for controlling
adaptive life management according to some embodiments; and
[0043] FIG. 13A-13J show a graphical user interface for controlling
adaptive life management according to some embodiments.
DETAILED DESCRIPTION OF THE INVENTION
[0044] A framework is provided for adaptively managing the life of
components. The framework provides a system for accurately
predicting component life, scheduling component inspections, and a
decision making process for maintaining and replacing components.
The inventors have recognized and appreciated that remaining
component life is inherently probabilistic and that using
information collected in a sequence of inspections may
significantly improve estimates of components life, improve
scheduling of inspections, and improve the decision making process
for performing conditions based maintenance (CBM) actions.
[0045] As used herein a "component" is any type of physical part or
device. In some embodiments, a component may be a constituent of a
device. Though, a device, regardless of its number of constituent
parts, may be a component. Examples of types of components include
rotorcraft components, fixed wing aircraft components, drill pipe
connections, oil pipelines, composite skins, and medical implants.
In some embodiments a component is a part of an aircraft, such as
an airplane, glider, or helicopter, or UAV. Though, it should be
appreciated that the framework may be used with components of any
suitable type.
[0046] Components may be made out of any suitable material or
combination of materials. For example and not limitation, component
may be made of materials such as metals, alloys, ceramics,
asphalts, transparencies, rubber, glass, cable bundles, composites,
and matrix/fiber materials such as carbon fiber reinforced
composites. Some components may be made up of a combination of
materials. For example, a component may include several material
layers. The layers may include different materials and may feature
materials of the same type but at different orientations with
respect to one another. For example, a component made from a fiber
based composite may include a stack up of multiple layers with the
same or differing orientations of the fibers. Practitioners may
refer to a component as a "critical component" if in-service
failure of the component is unacceptable.
[0047] Components may be shaped or used in such a way as to have
one or more features at which the fatiguing effects of the
operating conditions are more significant than other locations on
the component. As such, growth of flaws to a critical size at any
of these locations may represent the likely failure modes for the
component. Practitioners may refer to these features as "hot
spots," "control points," or "fatigue critical locations." Some
examples of hot spots may include bolt hole locations, connection
points, narrow regions, and other features of the component that
tend to be subject to increased damage rates, such as from higher
stresses under the component's operating conditions.
[0048] During operation a component's condition may deteriorate due
to the development and growth of flaws in the material of the
component, such as the growth of a crack or the development of
deleterious condition such as residual stress relaxation.
Deterioration of the components condition may result from the
material of the component having a lower residual strength. Such
damage to the component may develop near the component's hot spots.
Though, it should be appreciated that flaws may develop anywhere on
a component. For example, some flaws may be generated by impact
damage. While the presence of flaws may foreshadow the onset of
reduced functionality or failure of the component, actual reduction
in functionality of a component is not required for a flaw to be
present as many components are "over designed" to accommodate
damage without any reduction in performance. What constitutes
damage depends on the function and material of the component, more
particularly on the function of the component for which in-service
component failure is to be avoided. Examples of damage for
components providing mechanical strength made of metals and alloys
include metal fatigue, cracks, corrosion, thermal,
thermomechanical, and mechanical impact damage. As another example,
damage of components providing mechanical strength made of
matrix/fiber composites include cracks, impact damage to the matrix
and fibers, thermal damage, fatigue, machining effects such as
cutting and drilling, damage from mechanical and thermal overloads,
and environmental damage such as corrosion.
[0049] Adaptive life management may be performed for a single
component or a group of components. A group of components of the
same type that are being monitored are herein referred to as a
pool. A component life management system may perform life
management for more than one type of component.
[0050] Component lifetime may be measured in equivalent fatigue
cycles. Cycles may be defined in any suitable way such as to allow
a consistent comparison between, for example, different time
periods or different components. Examples of definitions for
counting cycles include ground-air-ground (GAG) cycles, and total
accumulated cycles (TACs). In some embodiments, cycles may be
measured as equivalent usage hours under prescribed operating
conditions. It should be appreciated that, unless otherwise stated,
use of the word time refers to a chronological progression that may
be measured in seconds, equivalent cycles, cycles, or any other
measure of chronology in a process.
[0051] The risk of failure of a component is the probability of
having a flaw grow beyond a critical flaw size. The critical flaw
size may, for example, be the flaw size at which the flaw growth
rate will increase to a point that will cause failure in the next
service period (that is before the next inspection opportunity).
Note that the critical flaw size may vary for different features of
the component. For example, failure for a crack may be defined as a
point at which the crack size reaches a prescribed crack depth
within a predefined set of locations at one or more critical
features on a critical component. In some embodiments, laboratory
tests may be performed to define the critical flaw size. Though, it
should be appreciated that the critical flaw size may be defined in
any suitable way.
[0052] FIG. 2 is a block diagram of an inspection system 200
according to some embodiments. Inspection system 200 includes a
component inspection module 220 and a computing system 240.
[0053] Component inspection module 220 obtains inspection data by
inspecting a component 260 and provides this inspection data to
computing system 240. In some embodiments, component inspection
module 220 includes sensors for performing a non-destructive test
(NDT) of component 260. The sensors may provide good repeatability
from scan to scan such that comparison between inspection data
taken at different cycles is practical. In some embodiments,
component inspection module 220 may use an electromagnetic based
sensing technology, an ultrasonic based sensing technology, or any
other suitable sensing technology or combination of technologies.
For example an eddy current sensor, such as a meandering winding
magnetometer (MWM) sensor, may be used to inspect components. As
another example a capacitance sensor, such as a dielectrometry
sensor, may be used to inspect the component.
[0054] In some embodiments, component inspection module 220
provides a high resolution imaging of a component in, for example,
one, two, or three spatial dimensions. NDT inspections may produce
a one, two, or higher dimension record/image that relates to the
material condition over a surface or volume of a component or other
material configuration. The inspection data may be digitized for
storage in a computer-readable storage medium. NDT inspection
methods that produce digital data may be referred to as "digital
NDT". The sensor may image some measure of damage or some property
of the material that is related to damage such as the
microstructure or micromechanical features of the component's
material. In some embodiments one or more electrical properties of
the component's material are imaged by the component inspection
module 220. The measured electrical properties may be related to
damage features.
[0055] Components 260 may be one or more components that are being
inspected. Components 260 may be any suitable type of component,
may be a pool of components of the same type, or multiple pools of
components. In some embodiments, components 260 are components of a
fleet of aircraft.
[0056] Computing system 240 may be any suitable type of computer
configured to receive and process sensor data from component
measurements. In some embodiments, computing system 240 comprises a
plurality of computers. The computers may be operably connected via
any suitable networking technology. In some embodiments, inspection
module 220 and a computing system 240 are integrated into a single
unit, for example, a handheld device. In some embodiments,
inspection module 220 and a computing system 240 may be separate
units. Though, inspection module 220 and a computing system 240 may
be provided in any suitable way.
[0057] Computing system 240 has a processor 241 operably connected
to a memory 242. Processor 241 may be any suitable processing
device such as, for example and not limitation, a central
processing unit (CPU), digital signal processor (DSP), controller,
addressable controller, general or special purpose microprocessor,
microcontroller, addressable microprocessor, programmable
processor, programmable controller, dedicated processor, dedicated
controller, or any other suitable processing device. In some
embodiments processor 241 comprises one or more processors. For
example, processor 241 may have multiple cores and/or be comprised
of multiple microchips.
[0058] Memory 242 may be integrated into processor 241 and/or may
include "off-chip" memory that may be accessible to processor 241,
for example, via a memory bus (not shown). Memory 242 may store
software modules that when executed by processor 241 perform a
desired function. Memory 242 may be any suitable type of
computer-readable storage medium such as, for example and not
limitation, RAM, a nanotechnology-based memory, one or more floppy
discs, compact discs, optical discs, volatile and non-volatile
memory devices, magnetic tapes, flash memories, hard disk drive,
circuit configurations in Field Programmable Gate Arrays, or other
semiconductor devices, or other tangible, non-transient computer
storage medium.
[0059] Computing system 240 also includes suitable input/output
(I/O) 243. I/O 243 comprises any suitable hardware and software for
interacting with computing system 240. For example, I/O 243 may
include a user I/O 252 and a network interface 253.
[0060] Network interface 253 may be any suitable combination of
hardware and software configured to communicate over a network. For
example, network interface 253 may be implemented as a network
interface driver and a network interface card (NIC). The network
interface driver may be configured to receive instructions from
other components of computing system 240 to perform operations with
the NIC. The NIC provides a wired and/or wireless connection to the
network. The NIC is configured to generate and receive signals for
communication over network. In some embodiments, computing system
240 is distributed among a plurality of networked computing
devices. Each computer may have a network interface for
communicating with other the other computing devices forming
computing system 240.
[0061] Computing system 240 may include one or more databases such
as signature library 244, condition progression database 245, and
inspection archive 251. The databases may be stored in memory 242,
though this is just an illustrative embodiment and other storage
locations are possible.
[0062] Signature library 244 is a library of sensor responses for
component flaws. Signatures may be generated from experiment,
analytical models, computer simulation, any suitable combination
thereof, or in any suitable way.
[0063] In some embodiments, a study is performed on simple elements
and/or representative fatigue test articles to generate crack
signatures. Simple elements or simplified elements are elements,
coupons or otherwise representative configurations of a material of
interest. Simple elements may be processed (e.g. fatigue tested,
heat treated, shot peened, machined) in a manner representative of
the behavior of interest and NDT data is recorded on the simple
elements at different times or stages within the process at
prescribed locations within the material volume. A representative
fatigue test article may be a simple element or a more complex
element that represents a fatigue critical location on a
component.
[0064] A suitable coupon may be made of the same material as the
component to be inspected. Baseline sensor measurements of the
coupon may be taken, for example, before a crack develops. The
coupon may then be fatigued by an applied cyclic load. The coupon
may be scanned periodically using the inspection sensor as a crack
develops. A secondary measurement technique may be used to
characterize the crack such that the resulting scan may be
identified with the "actual" crack characteristics. In some
embodiments, acetate replicas or fractography may be used. The
inspection sensor data is used to locate cracks in the replicas for
larger crack sizes so the earlier replicas can be used to locate
the cracks before sufficient sensor signal-to-noise existed for
reliable detection.
[0065] The secondary measurement technique may provide a direct
measurement of the flaw size. Though any suitable measurement
technique may be used. The secondary measurement technique may be
another non-destructive testing technique. Though, in some
embodiments a destructive measurement technique is used. It should
be appreciated that the secondary testing technique may not be
suitable for field measurements of the component. This may be due,
for example, to the time it takes and/or the cost of performing the
secondary measurement.
[0066] In some embodiments the flaw signatures are filtered. For
example, the flaw signatures may be baseline subtracted. That is,
the sensor response to the coupon prior to development of the flaw
may be subtracted from the sensor response to the coupon after
development of the flaw. The flaw characteristics may be known with
high accuracy by using secondary measurement techniques. In another
embodiment, a selected signature from the signature library may be
used to construct a digital filter to enhance the flaw response and
suppress noise.
[0067] Computing system 240 may also include inspection archive
251. Inspection archive 251 may store information related to
previous component tests, inspection schedules for the components,
history of condition based maintenance actions, predicted operating
conditions for the component, and any other suitable information
related to a component, pool or fleet. In some embodiments,
inspection archive 251 maintains information for a number of the
same type of component. For example, information may be stored for
each component in a pool. Inspection archive 251 may also store
statistics generated for a pool and information for different types
of components.
[0068] Condition progression database 245 stores the execution
results for inputs to a condition progression model, which is also
referred to as a phenomenological model. The execution results may
be tabulated by condition progression database 245 in the form of a
hyperlattice. A hyperlattice is an n dimensional nonlinear
parameter space generated from a phenomenological model for the
growth of component flaws. Here n may be a counting number (1, 2,
3, . . . ). In the special cases of n=2 and n=3 the hyperlattice is
referred to as a grid and a lattice, respectively. The hyperlattice
may be used as a look-up table. The phenomenological model and the
generation of a hyperlattice is discussed, for example, in
connection with FIG. 3, below. In some embodiments, condition
progression database 245 may store more than one hyperlattice.
Multiple hyperlattices may be stored when different ranges and/or
densities of input parameters are used to generate the
hyperlattices. Also, hyperlattices may be generated by different
models if different types of flaws may be present in a component.
For example, different crack morphologies may have different
hyperlattices.
[0069] In some embodiments, a hyperlattice is a database that is
computed using a phenomenological model where one dimension of the
hyperlattice is material condition and another dimension of the
hyperlattice is a measure of time (e.g., cycles or some
chronology). The material condition and measure of time may be
measurable, using a sensor or other means, within some finite
uncertainty. The hyperlattice may also includes at least two
additional properties selected for estimation. In one embodiment,
the two unknown properties are remaining life and stress. In
another embodiment, the properties are crack size and stress where
the crack size unknown to be estimated is the same as that measured
by the sensor. When the unknown is remaining life, the remaining
life may be defined as the time remaining to reach a critical crack
size, where the critical crack size may vary with the estimated or
predicted applied stress.
[0070] A lattice point is a data point in the hyperlattice
database. In some embodiments, a lattice point may include at least
four values, for example, two measured values and two values of
parameters to be estimated. The values of the parameters to be
estimated may be generated offline using the phenomenological model
for the range of possible values for the two estimated unknowns
over the range of the possible values of measured values.
[0071] Plot 410, shown in FIG. 4A, shows an example of at least a
portion of a hyperlattice along with some exemplary data to
illustrate some aspects of use of the hyperlattice according to
some embodiments. In this example, the material condition is crack
length which is plotted on axis 411. Time is measured in cycles and
is plotted on axis 413. A grid 420 is plotted on plot 410. The
parameters estimated by grid 420 are remaining life and stress.
Arrow 421 indicates the direction of increasing stress and arrow
423 indicates the direction of increasing remaining life. The crack
length may be estimated at inspection time t1 using sensor data.
Here the sensor data is characterized by a metric 415, a.sub.1. A
probability distribution 416 of crack lengths is estimated from
a.sub.1. A probability distribution 425 estimates the number of
cycles the cracked component was in service at inspection time t1.
Region 428 in the grid represents regime of likely stresses and
remaining life given probability distribution 416 of crack length
and probability distribution 425 of number of cycles. A second
inspection is also shown at time t2. Metric 417, a.sub.2,
characterizes the sensor response at time t2. A probability
distribution 418 of crack lengths is estimated from a.sub.2. A
distribution function for the number of cycles may also be
estimated for t2 (not shown). From the probability distributions
for the crack lengths and the number of cycles at t2 the stress and
remaining life of the component may be estimated. The estimated
stress and remaining life are represented by probability
distribution 427 and 426, respectively. From the lattice it can be
seen that the locus of critical crack sizes 422 (remaining life is
0%) predicted from the hyperlattice is at around 100 mils.
[0072] Computing system 240 may include computer executable
software modules, each containing computer executable instructions.
The software modules may be stored in memory 242 and executed by
processor 241, though this is just an illustrative embodiment and
other storage locations and execution means are possible. In some
embodiments, receiving module 246, filtering module 247, estimation
module 248, prediction module 249, decision module 250 and
reporting module 254 may be implemented as computer executable
modules. However, these modules may be implemented using any
suitable combination of hardware and/or software.
[0073] Receiving module 246 is configured to receive inspection
data from component inspection module 220. In some embodiments,
receiving module 246 interfaces with component inspection module
220 through a wired or wireless interface. For example, component
inspection module 220 may be connected to computing system 240 via
a USB, IEEE 1394 connection, through an Ethernet, Bluetooth or IEEE
802.11 network. In some embodiments, a computer-readable storage
medium, such as a compact flash disk is used. Though, inspection
data may be provided to computer system 240 in any suitable
way.
[0074] Once the inspection data is received by receiving module 246
the data may be passed to filtering module 247 for filtering. In
some embodiments, receiving module 246 stores the inspection data
in inspection archive 251.
[0075] Filtering module 247 may filter the inspection data to
enhance the observability of component conditions of interest and
to suppress indications that are not of interest. In some
embodiments, filtering module 247 performs baseline subtraction.
Baseline subtraction may be performed by spatially registering
earlier inspection data with the present inspection data and taking
the difference. Earlier inspection data may be obtained, for
example, from inspection archive 251. Filtering module 247 may also
filter the inspection data using signatures from signature library
244.
[0076] Estimation module 248 may estimate the current condition of
the component. The current condition may be estimated using
previous inspection data, hyperlattice look-ups, or in any suitable
way. In some embodiments, the current condition is described
probabilistically, for example, by a probability distribution
function or a cumulative distribution function. In some
embodiments, the distribution function is a Gaussian distribution
defined by a mean and standard deviation.
[0077] To estimate the current condition estimation module 248 may
identify the location of flaws on the component. Flaw sites will
promote damage evolution at a faster rate than locations without
such damage. The constellation of flaws and their respective types
and sizes may be recorded and stored in inspection archive 251.
Identifying current flaws may be facilitated in part by flaws that
were identified on the component as part of a previous inspection.
The location of flaws may be mapped and the evolution of the flaws
tracked across inspections. The flaws may be ranked, for example,
based on the risk of component failure they present.
[0078] Prediction module 249 predicts the future condition of the
component at a future time. In some embodiments, prediction module
249 performs a look-up and interpolation in condition progression
database 245 to predict the future condition or time. The
prediction model may be configured to select an appropriate
hyperlattice from condition progression database 245. Selection of
the hyperlattice may be based for example, on expert input, the
current condition, previous flaw detections, expected flaw
growth.
[0079] Prediction module 249 may include a rapid, multivariate,
nonlinear search tool. The search tool may generate real time
estimates of unknown properties of the condition of a component and
the uncertainty distribution for those properties.
[0080] The flaw sites, types, and sizes may stored by evaluation
module 248 in inspection archive 251 may be input to the
hyperlattice or the phenomenological models for identifying and
bounding the time that new damage sites appear on a component such
as a metal dynamic rotorcraft component, or a composite wing skin
or propeller blade or on an oil pipeline.
[0081] After the future condition and/or future time have been
predicted, decision module 250 may determine what action, if any,
should be scheduled or made for the component. In one such
embodiment, if the mechanical/impact damage level is below a
threshold that enables it to remain in service (even though it was
detected and documented by the sensing method) then it is valuable
to map and track these sites, and to record the time at which they
appeared. Then the damage evolution can be monitored for each site
to assess risk, and the possible interaction of sites that are
close enough to increase risk of failure, can be incorporated into
models. Decision module 250 may schedule a next inspection of
component, determine that the component should be replaced, and/or
determine that the component should be repaired. Inspections may be
scheduled at equal intervals or spaced in sequences that improve
flaw growth rate (derivative) estimation or in any other pattern
that improves estimation or prediction of component conditions.
[0082] Condition progression database 245 may be periodically
updated to reflect additional knowledge obtained through the course
of managing a pool of components. The underlying models on which
the hyperlattices are generated may be adjusted, for example, to
correct estimated parameters and assumptions that are better
understood. In some embodiments, a system 300, shown in FIG. 3, is
used to distribute condition progression database 245. In some
other embodiments, computer system 240 may include models for
generating the hyperlattices for condition progression database 245
(FIG. 2).
[0083] A reporting module 254 may be configured to generate reports
documenting the inspection, detection of flaws, estimated
conditions and predicted conditions, the action to be taken for the
component, and the like. Reporting module 254 may populate a
database that may be accessed by administrators and experts. In
some embodiments, the database may be accessed over a network. In
some embodiments, reporting module 254 generates a word processor
document report. The report may be printed out or stored on a
computer, for example, in association with the component.
[0084] As shown in FIG. 3, server 300 may be connected to one or
more inspection systems 200 (see also FIG. 2) via a network 310. An
updated version of condition progression database 245 may be
downloaded from server 340 to the respective inspection systems
200. In some embodiments, only some hyperlattices stored in
condition progression database 245 of server 300 may be downloaded
to particular inspection systems. The availability of hyperlattices
may be determined, for example, based on licensing
arrangements.
[0085] In some embodiments, inspection systems 200 may also upload
inspection data, statistics, component conditions, and the like to
server 340. Server 340 may have a processor 341, memory 342 and I/O
343 similar to those described for processor 241, memory 242, and
I/O 243 above (see FIG. 2).
[0086] Hyperlattices stored in condition progression database 245
may be generated by a condition progression module 344 which
provides a phenomenological model for predicting the evolution of
component damage or anticipating kinematic, static, dynamic
environmental or material changes in the component. The flaw growth
rate predicted by the model may depend on the operating conditions
incurred during each cycle by the component. Different operating
conditions may result in different time period for growing a flaw
to its critical size. Plot 400, shown in FIG. 4B, is a sketch of
two different flaw growth curves for two different operating
conditions. Specifically, plot 400 illustrates the growth in flaw
size (axis 401) as a function of the number of operating cycles
(axis 402). Curve 403 is a flaw growth curve for stress
.sigma..sub.0 and curve 404 is a flaw growth curve for stress
.sigma..sub.1. Both curves assume the same initial condition for
the component. As sketched, .sigma..sub.0>.sigma..sub.1. Also
marked in plot 400 are the detectable flaw size 405 and the
critical flaw size 406. The detectable flaw size is a flaw that can
be detected with a defined probability of detection (POD) with a
defined probability of false alarm (PFA). The POD and PFA of the
detectable flaw size may, for example, be specified to suitable
levels for a specific application.
[0087] The phenomenological model may predict component conditions
such as how flaw size affects the sensor response, property (such
as effective electrical conductivity or magnetic permeability)
variations with impact damage, thermal changes in electrical
properties, dielectric constant changes with thermal exposure, and
any other suitable property of the component and how the property
affects sensor response. The model may be component specific, and
may be tailored for the materials used to construct the
component.
[0088] The phenomenological model may be physics based (e.g.,
fracture mechanics model, fatigue model), system dynamics based,
parametric, logic based, empirical study based, or based on field
and production experience, or any suitable combination thereof.
Though, any suitable type of phenomenological model may be used. In
some embodiments, different phenomenological models are used to
model the evolution of different types of flaws. A crack, for
example, may have several different morphologies. A cracks may
develop as long and shallow discrete crack, a cluster of similarly
sized microcracks, or a cluster of variable sized small cracks with
one larger crack. The evolution of these crack morphologies may be
modeled using different phenomenological models and accordingly,
different hyperlattices may be produced. The models may also
account for the proximity of damage sites on a component as flaws
located sufficiently near one another may have a different damage
progression path than in isolation. In some embodiments, the
phenomenological models may be provided and modified by one or more
experts in the relevant technical arts.
[0089] The phenomenological model may be used to generate a
hyperlattice of conditions of the component. The input conditions
used to generate the hyperlattice from the model may be derived
from laboratory studies of the component's material, knowledge from
experts such as original equipment manufacturers (OEMs). For
example, input conditions may be determined from coupon studies of
crack initiation and growth in a representative environment.
Though, any suitable source may be used to determine the inputs for
the phenomenological model.
[0090] The phenomenological model and resulting hyperlattices may
be calibrated to improve their predictive power by using reference
part calibration or standardization techniques. In some
embodiments, server 300 may provide the uploaded information
collected from inspection systems 200 to the experts as feedback
for actual component damage evolution. Also, retired component may
fatigued to actual failure while collecting fatigue related data.
Such a test may be used to determine the actual remaining life of a
component. In some embodiments, a retired component may undergo
secondary testing (destructive or non-destructive) to determine the
actual condition of the component at retirement. This information
may be used to reconfigure the phenomenological models to better
agree with historical data, generate improved hyperlattices,
provide improved uncertainty estimates, usage estimates, initial
flaw size estimates, inclusion density, grain decohesion
propensity, surface roughness, residual stress, mechanical damage
conditions, and the like.
[0091] In some embodiments certain model parameters may be modified
to produce model outputs that agree with reference part calibration
data, fleet sensor measurements, values based on expert knowledge,
or the probability distribution of sensor measurements for a
similar component. The parameters may represent, for example, the
material condition such as residual stress distribution, assumed
inclusion density and assumed initial crack size distribution.
Though, the parameters may represent any suitable variable. In some
embodiments, the reference part calibration data represents the
condition of a component after a known number of cycles and given
stress level. Accordingly, the initial assumptions about the
material at completion of manufacturing (i.e., at 0 cycles) may be
adjusted such that the phenomenological model predicts the
condition of the reference part at the known number of cycles for
the given stress level. As a specific example, the assumed
distribution function (e.g., maximum likelihood value and
uncertainty) of the initial inclusion size may be adjusted to match
the distribution function of the condition of a pool of coupons or
components at the given time in the future and for a given stress
level. Though, other sources of the distribution function may be
used as well. For example, expert knowledge may be used to estimate
distribution functions for a defined number of cycles and given
stress level. Uncertainty may be selected with constructive and
destructive cumulative uncertainty from multiple sources such as
model input, operation conditions, sensor error, ground truth
errors in calibration data, recalibration data, and population
data.
[0092] In some embodiments, the hyperlattice is assumed fixed and
the inspection sensor responses, usage and other measured data are
calibrated to match the hyperlattice. In the case where the
hyperlattice is assumed fixed, inspection data may be taken using
the component inspection module on components or samples with known
properties. A transformation is applied to the inspection data such
that the transformed inspection data is in agreement with the
hyperlattice. In some embodiments, a transformation may include an
adjustment to the effective cycles to match the reference
calibration data to the hyperlattice. In some embodiments, flaw
size estimation filters are adjusted as part of the transformation
to match hyperlattice predictions and other ground truth data. The
determined transformation may then be applied to inspection data
that is taken on samples with unknown properties for other
estimation and prediction computations. Further aspects of this
calibration technique may be found in ASTM-E2338.
[0093] Server 300 may also include an expert management module 350
for managing the experts that define the phenomenological models of
condition progression module 344. For example, module 350 may
provide a tool for enabling a team of experts to work together to
refine the phenomenological models. In some embodiments, expert
management module 350 provides a web based interface and/or
portable device for experts to access inspection data, coupon data,
the current phenomenological models, hyperlattices, and any other
information relevant to defining the phenomenological models or
assessing its performance. Expert management module 350 may limit
information access to individual experts according to their
respective access rights. Some experts may be given a supervisory
role to control versions of the phenomenological models and
scrutinize the work of other experts to ensure the reliability of
the phenomenological models and the hyperlattices generated
therefrom.
[0094] Method 500, shown in FIGS. 5A-5B, is a method of adaptively
managing the life of a component. Method 500 may be implemented in
any suitable combination of hardware and software. For example,
method 500 may be implemented in inspection system 200. In some
embodiments, steps of method 500 are stored as instructions on a
computer-readable storage medium. When the instructions are
executed, the corresponding method step maybe performed. In some
embodiments, a graphical user interface may guide an operator
through performance of various steps in the method.
[0095] It should be appreciated that the steps of method 500 may be
performed in any suitable order and FIGS. 5A-5B merely illustrate
method 500 according to some embodiments. It should also be
appreciated that in some embodiments some steps of method 500 may
be optional.
[0096] At step 501, initial inspection data is taken for a
component. This initial data may be taken prior to putting the
component into service. For example, the initial inspection may be
done at the end of the manufacturing process. Inspection may be
performed for the entire part or may be limited to a set of
locations such as hot spots representative of the most probable
failure modes for the component.
[0097] In some embodiments, inspection data is collected during
manufacturing as well. For example, the component may be inspected
before and after certain manufacturing steps. A component may be
scanned before and after, heat treating, surface treatments (e.g.,
shot peening) and the like. In some embodiments, the same
inspection technique is performed multiple times. By taking
multiple measurements at each point, instrument and setup noise may
be suppressed by averaging. In some embodiments, inspection data
may be taken in multiple orientations. For example, a component
made of a material with anisotropic conductivity may be scanned in
multiple directions with a MWM-Array sensor to determine the
conductivity in different directions.
[0098] Plot 630, shown in FIG. 6A, illustrates an example of
inspection data on a component. In this example, the inspection
sensor is an MWM-Array sensor, and the surface conductivity is the
material property being plotted in grayscale. Axis 631 and 633 may
give the physical dimensions in the respective directions such that
the conductivity data may be associated with a particular point on
the component. Scale 635 shows the conductivity scale. In some
embodiments, the scan results are presented to the operator and the
operator may confirm the inspection.
[0099] In some embodiments, metadata is provided for the component
being inspected at step 501. The metadata may contain information
such as the type of component, a serial number for the component,
identification of a device the component is part of (if any), the
type of material the component is made of, the sensor being used to
obtain inspection data, the material of the component, any special
treatments performed to the component such as surface treatments,
any previous condition based maintenance actions performed on the
component, information about the operator, information about the
conditions of the measurement such as date, time, temperature and
humidity, risk tolerance levels, and any other suitable
information. The metadata may be used in the performance of method
500.
[0100] At step 503, it is determined whether the component is
acceptable to be put into service. The determination may be based,
for example, on the initial inspection data collected at step 501.
In some embodiments, the initial inspection data is processed to
identify areas of possible concern. For example, if the inspection
data indicates surface conductivity, a local drop in conductivity
may be indicative of a potential crack in the material at that
location. Indications may be ranked based on the likelihood that
they represent a flaw in the component. The indications may be
presented to an operator for review. Plot 637, shown in FIG. 6B,
shows an example of how indications may be represented for a
component. In this example the top five ranked indications 638 are
shown. However, any suitable number of indications may be ranked
and presented. Legend 639 presents the ranking. Here, zero
represents the unranked space of the component. Axis 631 and 633
provide reference to position on the component for plot 637.
[0101] In some embodiments, the operator is given the option to
determine how the indication should be treated. For example, the
operator may reject the component based on the indication, may
indicate that the indication is not actually a flaw, or may
indicate that the indication should be tracked in future
inspections. In some embodiments, the operator may provide a
comment regarding the decision. The comment may be stored, for
example, in metadata along with the inspection data. Window 640,
shown in FIG. 6C, illustrates an example user interface for the
operator. Window 640 shows the top indications 641, their position
642, and their sensor response 643, a ("a hat"). (Determining the
sensor response is discussed, for example, in connection with step
517, below.) A status field 644 allows the operator to select a
status for the indication (e.g., reject component, track
indication, ignore indication). Additionally a comment field 645
may be used by the operator to input information about the
selection. Once the operator is satisfied, "Validate" button 646
may be selected.
[0102] If the component is rejected at step 503, method 500 ends.
If the component is accepted at step 503, method 500 continues to
step 505.
[0103] At step 505, the next inspection time is set. The inspection
time may be an actual time or may be a number of cycles. The next
inspection time may be determined based on the initial inspection
data, fatigue tests performed on coupons and representative
components, expected operating conditions for the component once it
enters service, damage evolution models for the component. In some
embodiments, the next inspection time is set by assuming the
largest undetectable flaw and using the damage evolution model to
predict a time when the flaw has progressed to a certain level
(e.g., 20% of critical flaw size). The next inspection time may be
set as the predicted time. Confidence in the initial condition may
also affect selection of the next inspection time. In some
embodiments the next inspection time is chosen as a range. This
provides some flexibility as to when the inspection may take place.
This may be useful, for example, when the component and component
inspection module are only periodically collocated. For example, a
component inspection module may be located at an airbase where an
aircraft lands. In another embodiment, a pair of future inspection
times (or cycles) are selected to improve observability of flaw
growth rates.
[0104] In some embodiments, inspection after a certain number of
cycles may be desired, however, it may not be practical, or
possible measure cycles directly. Accordingly, an actual time may
be chosen for inspection based on a prediction of when the desired
number of cycles will be reached.
[0105] In one embodiment, at step 507, the component is put into
service. Once the component is in service it is periodically
inspected in accordance with steps 509-525. As shown in the flow
diagram of method 500, these steps form a loop that are repeated
until it is determined to retire the component.
[0106] At step 509, it is determined whether it is time for the
next inspection. When the next inspection time is easily countable
such as for an actual time or a number of GAG cycles step 509 may
be determined directly. In some embodiments, it is determined that
it is time for the next inspection only if the actual number of
cycles is within an inspection range and the component and
component inspection module are collocated. Of course, if
inspection is an actual time (e.g., Jun. 5, 2009) it may be
determined that the time for inspection is at that date. Step 509
loops until it is determined that it is time to inspect the
component. The time between inspections is not necessarily equal.
It may be set, for example, based on risk of failure before the
next inspection.
[0107] At step 511, the component is again inspected. Inspection
may be performed in ways similar to those described in connection
with step 501. The inspection may be performed using a component
inspection module such as component inspection module 220 (FIG. 2).
As before multiple measurements at each location on the component
may be taken to average out noise. Inspection data may be
presented, for example, in ways similar to plot 630 (FIG. 6A).
Though, it should be appreciated that inspection data may be
presented in any suitable way.
[0108] At step 513, the inspection data obtained at step 511 is
spatially registered with inspection data obtained during a
previous inspection. Spatially registering the inspection data
aligns the inspection data spatially so that the inspection data at
the same location on the component an be compared to one another
between the two or more different inspections. Spatial registration
may also be made with reference to positions on the actual
component so that the inspections data can be understood with
reference to the physical component.
[0109] In some embodiments, the component may have markers that
have a unique signature when scanned by the component inspection
module. FIG. 6D shows a component 600 with markers 610 and 620. The
type of marker being used may depend on the inspection technology.
For example, for a conducting part on which inspection data is
collected using an MWM array, markers 610 and 620 may be marked
using an insulating tape. The presence of the insulating tape may
produce a predictable response that will be present in each
measurement of the component. FIG. 6E shows inspection data 610 and
inspection data 620. Both sets of inspection data clearly image
markers 610 and 620. In inspection data 610, markers 601 and 602
appear as marks 611 and 612, respectively. In inspection data 620,
markers 601 and 602 appear as marks 621 and 622, respectively. The
images are spatially registered by manipulating inspection data 620
such that marks 621 and 622 are aligned with marks 611 and 612,
respectively, of inspection data 610. In another embodiment,
markers may be existing patterns in the sensor data or the edge of
a part or another such geometric feature.
[0110] It is noted that the imaging of markers 610 and 620 may also
be used to correct for lost position data. For example, in some
embodiments a sensor is scanned over the surface of a component. To
correlate the sensor data with the physical location of the sensor
a position encoder may be used. The position encoder may be, for
example, an encoder wheel or an optical tracker (e.g., light
emitting diode and photodiode). If the position encoder seems
inconsistent with the scan data such as when the encoder wheel jams
or loses contact, the detected markers may be used to estimate the
location of the sensor.
[0111] In some embodiments the markers have a shape that enables
alignment in multiple directions. Though, in some embodiments,
alignment may be achieved by resolving the location of multiple
markers.
[0112] At step 514, baseline subtraction is performed between
spatially registered images. In some embodiments, other forms of
baselining are performed and, generally, baselining may involve any
suitable mathematical function and is not limited to
subtraction.
[0113] Baselining may suppress manufacturing variations while
enhancing the observability of flaws in the component. For example,
manufacturing variations may produce changes in an observable
material property (e.g., conductivity) that are on the same order
as changes produced from, say, impact damage. Baseline subtraction
will enable the impact damage to be observed, because of the change
in properties between the initial inspection data and the later
inspection data. The manufacturing variations in a component will
not have changed, however, and thus will be suppressed by baseline
subtraction. In one such embodiment, subtraction of a previous
image (spatially registered to the first image from the same
digital NDT method) other than the baseline is performed to enhance
noise suppression or improve crack detection and crack growth rate
estimation. In one such embodiment, combinations of two or more
images are used to create a functional value of each image location
that is then used for estimation and prediction. Where the
functional value derived from the data at the same location in two
or more images. The functional value is also derived at additional
locations within at least two images.
[0114] To perform the baseline subtraction the earlier inspection
data may be subtracted from later inspection data on a
point-by-point basis. In some embodiments the resolution and/or
spatial position of the samples may be different between the sets
of inspection data. Any suitable interpolation method may be used
to produce the difference. In some embodiments, the initial
inspection data captured at step 501 is used as the baseline to
which all future inspection data is compared. In some embodiments,
the inspection immediately preceding the current inspection is used
as the baseline. Though, any suitable inspection data may be used
as a baseline.
[0115] Plot 700, shown in FIG. 7A, illustrates the use of baseline
subtraction to enhance the visibility of a crack in a titanium
alloy (Ti-6Al-4V). Curve 701 shows the normalized conductivity
after 15,000 stress cycles. Curve 703 shows the normalized
conductivity after 21,000 stress cycles. Both curves 701 and 703
are obtained by averaging 5 repeat scans using a MWM sensor at the
respective time. Plot 710, shown in FIG. 7B, shows the change in
conductivity curve 711 between 15,000 and 21,000 cycles. The drop
in conductivity from the baseline level at around 0.52 inches
creates a indicator for a crack that would not be directly
identified from curve 703 alone.
[0116] It should be appreciated that the operation of baseline
subtraction computes the change of the material properties.
Knowledge of the time difference between the current inspection and
the inspection data used as the baseline (where the baseline is
taken from either from digital NDT data at t0 or some other earlier
time) may be used to estimate the rate of change in material
properties; that is the first derivative. It should also be
appreciated that as more inspection data becomes available, for
example after each inspection time, higher order derivatives may be
estimated. For example, after data from a third inspection becomes
available, the rate of change (first derivative) may be estimated
using the first in-service inspection and the initial inspection
and may again be estimated using the first and second in-service
inspection. Accordingly, the second derivate, that is the rate of
change in the rate of change, may be estimated. Further, as more
inspection data becomes available, the estimates may be
refined.
[0117] At step 515, the inspection data is filtered using a
signature from a signature library. As discussed above, the
signature may be obtained from coupon tests of known flaws. In some
embodiments, the inspection data is searched for a match to each
signature. A match may have the same shape as the signature. A
threshold may be set to determine whether a match has been found.
If the same location on a component matches multiple signatures,
the best match may be chosen for that location. Signatures may be
matched at multiple locations on a component. Applying signatures
to inspection data results in a POD curve that is steeper than for
typical testing methodologies. In other words, flaw sizes below the
desired detection threshold are suppressed, while flaw sizes above
a desired threshold are detected more readily. In one such
embodiment, a function of multiple signatures, perhaps at different
frequencies or for different crack sizes, is used to filter the
inspection data.
[0118] At step 517, the current condition of the component is
estimated. The current condition may be estimated by characterizing
the (filtered) sensor response and using the characterization to
look-up the component condition. In some embodiments the sensor
response is characterized by a feature height, a feature area, a
half-height width, a crack correlation, or in any other suitable
way. The feature height may be defined as the peak change in
response at a feature relative to the surrounding response. The
half-height width may be defined as the width of the feature at
half the height. To illustrate, plot 800, shown in FIG. 8A,
sketches a sensor response 801 near a feature. Height 803 is the
change in sensor response from line 805 to point 807. The
half-height width 809 is the distance across the feature at half
way up the height 803. These metrics for characterizing a feature
observed in inspection data are illustrative. It should be
appreciated that any suitable metric may be used to characterize
inspection data.
[0119] Once the inspection data is characterized, a mapping may be
used to determine the component condition. In some embodiments, the
mapping is determined experimentally. For example, the sensor
response may be measured and characterized for various damage
feature sizes in a coupon test. This data may be used to determine
the condition of a component by using the sensor response on the
component to compute the distribution of crack sizes that is most
likely to have produced the sensor response.
[0120] FIG. 8B shows a plot 810 of example data for a coupon test.
Axis 811 represents the characterized sensor response, a ("a hat").
Axis 813 represents a characteristic of the flaw. For example, the
characteristic of a crack may be the crack width, crack depth,
crack length, crack volume, or any other suitable characteristic or
combination of characteristics. In some embodiments, "a" represents
the density of cracks. Coupon data 814, represented by the square
blocks, may be obtained by performing sensor measurements on flaws
that have been characterized using a secondary measurement
technique. From the coupon data the correlation between the sensor
characteristic response and the characteristic of the flaw may be
determined. From the sample data, probability distribution
functions may be estimated both for "a" and a.
[0121] Also shown on plot 810 is a sensor response 815 for a flaw
with an unknown characteristic size, "a". Using the sample data the
expected value of the flaw size, "a" may be determined for the
sensor response 815. Superimposed on plot 810 is a probability
density function 816 representing the probability density 817 of
the different flaw size characteristics. For illustrative purposes,
sensor response 815 is plotted along axis 813 at the expected value
for "a".
[0122] In some embodiments, the flaw may be too small to reliably
detect using only the inspection data from the current inspection.
To determine whether the flaw detected is reliable a threshold may
be set for the likelihood that the flaw is smaller than a certain
size. For example, it is determined that insufficient information
exists to reliably detect a flaw using the current inspection data
if the probability of detecting the flaw is below 90%. When
insufficient information exists to reliably detect the flaw using
the inspection data from the current time, an enhanced response
from the inspection may be generated by using inspection data from
the current inspection and one or more previous inspection
times.
[0123] Returning to method 500, at step 519 the future condition of
the component is predicted. In some embodiments, the future
condition is predicted at a proposed next inspection time. In some
embodiments, the future time at which the component condition will
have deteriorated to a certain point is predicted. In some
embodiments, the method produces a risk of failure as a function of
subsequent usage cycles. The prediction made at step 519 may
produce a maximum likelihood value and an uncertainty value. In
some embodiments, the prediction is simply described by a
probability distribution function for the future condition or
future time.
[0124] Prediction of the future condition may be facilitated by
using a mechanism other than direct execution of the
phenomenological model. For example, a precomputed database such as
the condition progression database 245 (FIG. 2) may be used. In
some embodiments, use of a precomputed database allows for real
time determination of the future condition, while use of the
phenomenological model to predict the future condition may take a
considerably longer time. Here, real time is understood to be a
time period of a few minutes. For example, two minutes or less.
Though, in some embodiments, a real time prediction of the future
condition may be a table look-up from the precomputed database in a
few to several seconds.
[0125] In some embodiments, the estimated current condition and
estimated operating condition of the component are described
probabilistically. That is, an uncertainty may be associated with
the values. If inspection data from multiple inspection data is
available, the condition at the respective inspection times may be
described probabilistically. Accordingly, the output future
condition of the component may also be described probabilistically.
A hyperlattice that may be used to account for uncertainty may also
be referred to as a fuzzy hyperlattice.
[0126] In some embodiments, the estimated current condition, one or
more estimated and then predicted operating conditions, including
the predicted number of equivalent cycles at the next inspection
time are used to predict the future condition. Though, any suitable
inputs may be used to predict the future condition. Each of these
input parameters may be described probabilistically as shown by
plots 901-911 in FIG. 9A. Specifically, plots 901, 903, and 905
show example sketches of probability density functions for the
estimated current condition, a; predicted operating conditions, a;
and predicted number of cycles at the next inspection time,
c.sub.t2. The equivalent representation, sketched in plots 907,
909, and 911, respectively, are cumulative distribution
functions.
[0127] The probability distribution of the current condition, "a",
may be determined in ways discussed, for example, in connection
with step 517. The probability distribution of the predicted
operating conditions, .sigma., may be predicted based on past
operating conditions, actual operating conditions of components in
the same pool, a schedule of operations for the component (or
device), fleet history, expert analysis, or in any suitable way.
The probability distribution of the predicted number of cycles at
the next inspection time may be estimated from fleet history,
expert analysis, a schedule of operation for the component (or
device), or in any suitable way. Of course, if cycles may be
measured directly and operation stopped for inspection after the
desired number of cycles, it may be possible that there is little
or no uncertainty in the number of cycles at the next inspection.
But, typically the equivalent number of cycles, i.e., the cycles
would be equivalent to a defined number of controlled loading
cycles with an assumed spectrum, is not known with little or no
uncertainty.
[0128] As illustrated the block diagram shown in FIG. 9B, the
inputs, in this example a, .sigma., and c.sub.t2, may be input to
prediction module 248, which in turn uses a hyperlattice in
condition progression database 245 to estimate the components
condition at the next inspection time, a.sub.t2. FIG. 9C shows a
sketch of an example cumulative distribution function of
a.sub.t2.
[0129] The probability distribution of the predicted future
condition may be determined by performing a series of look-ups in a
hyperlattice stored by the condition progression database. In some
embodiments, a rapid Monte Carlo method, or other such distribution
estimation method, is used to predict the distribution function for
the future condition from the hyperlattice by randomly selecting
the input conditions in accordance with their respective
distribution functions. In one embodiment, n values (e.g., n=100)
are selected for each of the m inputs in accordance with the
respective input's distribution function. The n.sup.m combinations
are input to the hyperlattice to produce n.sup.m outputs. Though,
the number of samples for each input may be chosen in any suitable
way (e.g., independently). The output values describe the
probability distribution of the output variable. The output values
may be represented, for example, as a cumulative distribution
function such as that shown in plot 913 (FIG. 9C). Though, the
output values may be presented in any suitable way.
[0130] In some embodiments, the probability distribution of the
future condition may be estimated by performing multiple look-ups
in the hyperlattice where each look-up uses inputs from the
distribution function of the current and possibly previous
conditions of the component. The resulting outputs are weighted in
accordance with the likelihood of the particular input conditions.
In this way a probability distribution may be estimated for the
future conditions.
[0131] In some embodiments, the cumulative distribution functions
are divided into quantiles as illustrated for plot 907 in FIG. 9A.
For example, each of the m inputs, the respective cumulative
distribution function may be divided into n quantiles (e.g.,
n=100). As above the n.sup.m combinations are input to the
hyperlattice to produce n.sup.m outputs which thus represent the
probability distribution of the output variable.
[0132] It should be appreciated that the future condition may be
performed for each indication separately on a component, or the
future condition may be estimated for the entire component. That
is, the future condition of the component may be determined by
predicting the future condition of each indication, or the future
condition of the component may be estimated taking in account the
culmination of indications on the component.
[0133] Although, the individual confidence level in the current
and/or previous component condition estimates may be below usual
POD and PFA rates, the trend of the condition estimates may provide
a reliable basis for estimating future states.
[0134] Plot 1000, shown in FIG. 10A, illustrates an operation that
may be performed for the hyperlattice lookup. Specifically, plot
1000 shows the progressive growth of a flaw (axis 1001) as a
function of the number of cycles (axis 1003) the component was
used. The relationship between flaw growth and usage cycles is
predicted for different operating conditions. The current condition
of the component is generally referenced by area 1010 of plot 1000.
The solid lines 1030-1036 represent paths of constant operating
conditions for the component. The lines represent equal probability
quantiles. The dashed lines in area 1010 and area 1020 also
represent quantiles for the current and future condition,
respectively. It should be appreciated that the smaller the
simplex, the higher the probability the condition falls at that
approximate simplex value. In some embodiments, identifying the
future condition comprises identifying the smallest simplex. While
plot 1000 illustrates a two dimensional hyperlattice, it should be
appreciated that use of quantiles to divide grids and identify high
probability outcomes via the smallest simplexes may be extended to
higher dimensional hyperlattices.
[0135] In some embodiments, the probability distribution function
of the future condition is displayed on a screen for a user to
review. In some embodiments, the display is similar to plot 1000.
Though, any suitable representation of the future condition may be
shown. Additionally, the probability of failure may be displayed,
for example, as show in plot 1050 of FIG. 10B. Specifically, plot
1050 shows curve 1051 which is the probability of failure as a
function of the number of cycles. The current time 1053 and next
inspection time 1055 may also be indicated. Though, any suitable
representation of the probability of failure may be used.
[0136] After predicting the future condition, at step 521, an
action is recommended based on the available information about the
component, such as the estimated current condition and predicted
future condition. Though, in some embodiments, the determination at
step 521 may be based only on current and previous conditions (step
519 omitted). For example, a threshold flaw size may be set and if
exceeded by the current condition a CBM action is performed (step
523) or the component is retired (step 527).
[0137] In some embodiments, the action is determined based on the
risk of failure before the next inspection. For example, if the
risk of failure is below a first damage tolerance limit, say
0.0001% risk of failure before the next inspection, the component
may be allowed (method 500 continues to step 525). If the risk of
failure is above the first damage tolerance limit, but below a
second damage tolerance limit, say 0.0005%, the component may be
repaired using a CBM action. If the risk of failure is above the
second damage tolerance limit, the component may be retired. In
some embodiments, the accept/reject/repair decision is based on the
risk of failure before the next inspection or a combination of the
crack size distribution estimated for the current time and the
projected risk of failure before the next inspection. In some
embodiments, the decision is based on the statistical risk for
individual components. In another embodiment, the decision is based
on the risk of failure for a fleet of components based on either
100 percent inspection of each component, or inspection of a
subpopulation of such components. While yet another embodiment, the
crack depth may determine whether a CBM action may be allowed along
with associated risks.
[0138] If it is determined at step 521, that a condition based
maintenance (CBM) action should be taken, method 500 continues to
step 523. At step 523 a suitable CBM action is taken. After
completing the CBM action, method 500 may return, for example, to
step 517 to re-estimate the current condition of the component,
predict its future condition, and determine whether the CBM
achieved the desired affect.
[0139] If it is determined to accept the component as is, method
500 continues to step 525, at which the next inspection time is
set. The next inspection time may be set, for example, in ways
similar to those discussed in connection with step 505. Though, the
additional information obtained through the subsequent inspections
may enable the condition of the component to be projected more
accurately.
[0140] If it is determined to reject the component, method 500
continues to step 527. At step 527 the component is retired and
method 500 ends.
[0141] In some embodiments of method 500, inspection data may be
obtained at the time of inspection (e.g., steps 501, 511) with a
load, such as mechanical or thermal loads, intentionally applied to
the component. For example, a composite propeller might be
inspected with a high-resolution MWM-array with and without a
controlled applied load, at two or more different inspection times.
The inspection data at each inspection time may be subtracted for
the two different loads (i.e., high load image minus low load
image) and then differenced again in time to obtain an image of the
change in the load difference images associated with impact events.
The change in the images may be used as an input for searching the
hyperlattice and predicting a future condition of the component and
the uncertainty associated with that condition.
[0142] Though method 500 has been described for time sequenced
inspection data, it should be appreciated that inspection data may
be sequenced in any other suitable way.
[0143] While method 500 has been described for a manufactured
component, it should be appreciated that the method may be applied
to components for which manufacturing data is inappropriate or
unavailable. For example, a local medical condition, such as a
tumor or the bond integrity for a repaired joint, may be inspected
at an initial time. A subsequent inspection may use the initial
inspection as a baseline.
[0144] The inventors have further recognized and appreciated that
the phenomenological models used to generate hyperlattices may be
used for estimation of not only future condition but also the
previous condition of a component. This may be useful when the data
has been censored. That it, there are gaps in the historical record
for a component's life. Censored data may exist, for example due to
data loss.
[0145] Further, properties of the component at the time of
manufacturing may be estimated by determining what initial
conditions would lead to the observed current condition of the
component. Such root cause information may be useful for
determining, for example, material properties at the time of
manufacturing that may only be investigated through expensive or
destructive testing techniques. Accordingly, the framework may be
used to estimate missing data or determine the root cause of the
current conditions.
[0146] FIG. 11 shows a flow diagram of a method for adaptively
managing the life of a component according to some embodiments.
[0147] A graphical user interface (GUI) that may serve as a front
end for method 500 (FIGS. 5A-5B) is presented with reference to
FIGS. 12A-13J. Though, it should be appreciated that any suitable
user interface may be used. Further, it should be appreciated that
in some embodiments, method 500 is automated and may not require
regular interaction with an operator.
[0148] The GUI provides an environment for component adaptive life
management (CALM). For the purposes of illustrating the CALM
environment GUI, an example of a metal component that develops
cracks is discussed. Though, the CALM environment may be used for
any suitable type of component or flaw type, or a process other
than damage evolution such as heat treatment, forming, machining,
curing, welding, or a medical procedure progression.
[0149] According to some embodiments, the CALM environment is
implemented by one or more modules. The modules that are used
during inspection of a component may be implemented to operate
within a data acquisition and processing application such as the
GridStation.TM. environment available from JENTEK Sensors Inc.,
Waltham, Mass. The modules may include a supervisor module that
guides the operator through the process, and one or more plug-in
modules that may implement, for example, data processing,
statistical analysis, and automatic report generation.
[0150] According to some embodiments, there are several plug-ins
that implement various stages of the data processing. A plug-in
named "Find Indications" may be used at the baseline (t0) stage and
it identifies the locations and magnitudes of indications based on
changes in the measured material property, for example, a localized
drop in conductivity.
[0151] A plug-in named "Find Indications 2" may be used at the t1
stage of the inspection process. Find Indications 2 finds
indications after baseline subtraction of the baseline data taken
at t0 and uses shape filtering, for example, using signatures from
signature library 244 (FIG. 2).
[0152] A plug-in named "CALM CDF(a)" may be run after Find
Indications 2. For each indication identified by the plug-in "Find
Indications 2", CALM CDF(a) uses "a vs. a" statistical analysis to
compute a cumulative distribution function for the crack size at
this indication at time t1 (i.e., cdf(a)|t1). CALM CDF(a) then runs
this CDF, together with CDFs on stress and usage cycles, through an
appropriate hyperlattice in two separate ways: (1) generate
cdf(a)|t2 that computes the expected crack size distribution at
this location at a future time t2 given by the CDF(usage) provided;
and (2) generate cdf(usage)|a2 that computes the probability
distribution of usage cycles for the crack at this location to grow
to a predetermined threshold size.
[0153] A "Reporting" plug-in may be used to automatically generate
reports based on the outputs from the other plug-ins.
[0154] The supervisor module may generate the GUI and other visual
elements of the software environment encountered during an
inspection. An operator will begin with the window 1200 shown in
FIG. 12A. Here there are three links to choose between, depending
on whether this is a baseline scan (t0), a first inspection scan
(t1), or a subsequent inspection scan (t2+).
[0155] If the operator clicks on the Baseline Inspection link in
window 1200, he is shown the steps that to be completed in a
baseline inspection, as shown in window 1201, FIG. 12B. The next
step is shown in bold and with a yellow arrow. This is the step
that will also be reached by clicking on the "Next" button.
[0156] In this step the operator is asked to fill in component
metadata, as shown in window 1203, FIG. 12C. If the operator clicks
on the purple action button, the metadata table in GridStation
opens (unless already open). An example of metadata in shown in
table 1205, FIG. 12D. The operator may fill out the second column
of table 1205. The fields in this table may be customizable. The
metadata in table 1205 may be saved with any data acquired as part
of the inspection and is also included in the automatically
generated reports.
[0157] After the operator fills out the metadata table, he is taken
back to the list of steps, as shown in window 1207, FIG. 12E.
[0158] The next step in the procedure is to acquire the baseline
scan data on the component. The supervisor provides the operator
with a set of instructions, as shown in window 1209, FIG. 12F.
[0159] After the scan data is acquired, the operator is instructed
to save the data, as shown in windows 1211 and 1213 of FIG. 12G and
FIG. 12H, respectively. When the operator clicks on the "Record
baseline data" in window 1213, he is presented with a dialog to
save the baseline data.
[0160] After the data has been acquired and saved, it is time to
run the baseline indications algorithm (the plug-in named "Find
Indications") as shown in window 1215, FIG. 12I and window 1217,
FIG. 12J. The algorithm begins data processing after the operator
clicks on the "Run Baseline data algorithm" shown in window
1217.
[0161] After the plug-in has finished executing, the data is
presented in visual form as C-scans, as shown in the screen-dump in
window 1221, FIG. 12L. These C-scan images may also be included in
the automatically generated report. In window 1223, FIG. 12M, the
operator is presented with a table that shows a list the most
likely indications, as determined by the Find Indications module.
This is shown in window 1219, FIG. 12K. The table lists the
physical location of the indications, the magnitudes (a), and the
status. The status may be either "Reject" or "Track", based on
whether the indication is above a certain threshold that may be
specified as part of the plug-in configuration and included in the
automatically generated reports. In window 1219, the operator has
the opportunity to change the status of the indications, or to set
the status to "Invalid" which is used to let the environment know
that this indication should not be retained for further tracking.
The operator can also enter comments for each indication, which are
retained with the data. After this the operator is asked to
validate the list of indications by clicking on the "Validate
button".
[0162] If the status of any of the indications in the table in
window 1219 are "Reject" after validation by the operator, then the
supervisor module informs the operator that the component has been
rejected and prompts him to create an NDT report, as shown in
windows 1223 and 1225, FIG. 12M and FIG. 12N, respectively. If this
is the case, this completes the inspection of this component and
the operator is shown the screen in window 1238, FIG. 12R.
[0163] If, on the other hand, none of the indications have a
"Reject" status, the operator is given the next set of steps, as
shown in window 1227, FIG. 12O. These steps include saving the
results of the data analysis for future inspections (window 1229,
FIG. 12P) and generating an NDT report (window 1231, FIG. 12Q).
After these steps are completed the baseline scan stage of the
inspection is complete and the operator is shown the screen in
window 1238, FIG. 12R.
[0164] Clicking on "Create NDT report" button in window 1227 (FIG.
12O) runs the reporting plug-in which automatically generates an
appropriate report.
[0165] After the operator clicks on the First Inspection (t1) link
in window 1201, FIG. 12A, he is shown the steps that must be
completed in an inspection at time t1, assuming that data from a
baseline scan is available, as shown in window 1235, FIG. 12S.
[0166] In this case no metadata is required of the operator,
because it was already filled out at the time of the baseline scan.
The operator is, however, always able to edit the data in the
metadata table and the edited date will be saved in future data
files and included in automatically generated reports.
[0167] Windows 1237, 1239 and 1241 (FIGS. 12T-12V) walk the
operator though the steps to take a scan on the component and store
the scan data. These steps are similar to the steps in the baseline
stage, described previously.
[0168] After the data has been acquired and saved, it is time to
run the data processing and statistical algorithms (the plug-ins
named "Find Indications 2" and "CALM CDF(a)") as shown in windows
1243 and 1245 of FIG. 12W and FIG. 12X, respectively. The
algorithms begin data processing after the operator clicks on the
"Run Algorithm" button shown in window 1245, FIG. 12X.
[0169] After the plug-ins have finished executing, the data may be
presented in visual form as C-scans and statistical CDF function
curves, as shown in the screen-dumps in window 1247, FIG. 12Y and
window 1249, FIG. 12Z. Note that for clarity in these figures some
of the windows have been hidden (including the supervisor window)
so that the data views can be shown. These C-scan and graph images
may also be included in the automatically generated report. Within
the supervisor window, the operator is presented with a table that
shows a list of the top five most likely indications, as determined
by the algorithm. This is shown in window 1301, FIG. 13A. The table
lists the physical location of the indications, the magnitudes (a),
and the status. The status can be either "Reject" or "Track", based
on whether the indication is above a certain threshold, specified
as part of the plug-in configuration and included in the NDT
reports. In the screen shown in window 1301, the operator has the
opportunity to change the status of the indications, or to set the
status to "Invalid" which is used to let the software know that
this indication is not "real" and should not be retained for
further tracking. The operator can also enter comments for each
indication, which are retained with the data. After this the
operator is asked to validate the list of indications by clicking
on the "Validate button".
[0170] If the status of any of the indications in the table in
window 1301 are "Reject" after validation by the operator, then the
supervisor informs the operator that the component has been
rejected and prompts him to create an NDT report, as shown in
window 1303, FIG. 13B and window 1305, FIG. 13C. If this is the
case, this completes the inspection of this component and the
operator is shown the screen in window 1317, FIG. 13I.
[0171] If, on the other hand, none of the indications have a
"Reject" status, the operator is given the next set of steps, as
shown in window 1307, FIG. 13D. These steps include saving the
results of the data analysis for future inspections (window 1307,
FIG. 13D and window 1309, FIG. 13E), generating an NDT report
(window 1305, FIG. 13C), and setting the next inspection interval
based on the statistical analysis through the fuzzy lattice.
[0172] After these steps are completed the baseline scan stage of
the inspection is complete and the operator is shown the screen in
window 1317, FIG. 13I. After finishing work with the supervisor,
the operator is instructed that the process is over (window 1319,
FIG. 13J).
[0173] As already discussed, for each indication identified by the
plug-in "Find Indications 2", the plugin "CALM CDF(a)" uses the "a
vs. a" statistical analysis to compute a cdf(a)|t1 cumulative
distribution function (CDF) for the crack size at this indication.
This result is shown as a separate curve for each indication, as
seen in window 1249, FIG. 12Z (upper left). This analysis uses a
vs. a data gathered as part of this effort on Titanium alloy
coupons and the statistical data that characterize this correlation
are input as part of the plug-in configuration.
[0174] It then runs this CDF, together with CDFs on stress and
usage, through the fuzzy lattice to generate cdf(a)|t2 that
computes the expected crack size distribution at this location at a
future time t2 given by the CDF(usage) provided. This result is
shown in the curves in window 1249, FIG. 12Z (lower left). In the
example presented here, the stress and usage input CDF
distributions were assumed to be normal, with means and standard
deviations as shown in the plug-in configuration, in this case set
to 415.+-.42 ksi and 2000.+-.200 cycles respectively. The plug-in
allows for non-normal distributions to be included as separate data
files.
[0175] The plug-in also generates cdf(usage)|a2 that computes the
probability distribution of usage cycles for the crack at this
location to grow to a predetermined threshold crack size. This
threshold crack size is part of the plug-in configuration, set to
79 mils (2 mm) in this example. The results of this analysis are
shown in the curves in window 1249 of FIG. 12Z (lower right). This
CDF(usage) distribution for the highest ranking indication
(indication 1) is what is used to set the next inspection interval,
as indicated in window 1315, FIG. 13H, given a tolerable risk level
for failure before the next inspection.
[0176] Clicking on the "Create NDT report" button in window 1305,
FIG. 13C runs the reporting plug-in which automatically generates a
report such as a Word document.
[0177] Flaw Size Distribution Inferred From NDT Inspection
Results
[0178] A mean relationship between NDT signal and flaw sizes is
established utilizing coupon data. However, individual flaw signals
will vary about that mean relationship. Thus, from a population of
flaws of the same size a distribution of sensor measurement signals
would be produced. Although from the same size flaw, if the mean
relationship was used for each of those signals to estimate a
corresponding flaw size, the resulting estimated flaw sizes would
be distributed about the actual flaw size. The process developed
and specified here may be used to quantify the resulting
uncertainty in estimating flaw sizes that is induced by signal
variation. Though, uncertainty may be quantified in any suitable
way.
[0179] The general technique of quantifying probability of
detection (POD) for an NDE process based on signal data is referred
to as an "a-hat" versus "a" analysis. This nomenclature comes out
of the first presenters of the methodology using the variable "a"
to refer to flaw size with "a-hat" referring to the signal data.
The methodology is that of regression in which the signal is
related to the flaw size through some relationship. The most
commonly used relationship is that that, on average, the logarithm
of the signal has a linear relationship with the logarithm of the
flaw size. That is,
E[ ln(S)]=.beta..sub.0+.beta..sub.1ln(a), (1)
where S denotes the signal (often written .sup.a, thus the name
a-hat) and E is the expectation operator.
[0180] Equation (1) is not enough to quantify the reliability
associated with the NDE process, as it only models an average
relationship of signal with flaw size. A full specification of the
distribution of the signal may be modeled by adding an error term
that captures the difference between the signal relationship and
the mean line. That is,
ln(S)=.beta..sub.0+.beta..sub.1ln(a)+.di-elect cons., (2)
where .di-elect cons. is now a random variable with mean 0. The
full probabilistic nature of the signal in this representation is
captured in the mean .beta..sub.0+.beta..sub.1ln(a) and the
distribution assigned to the zero mean random variable .di-elect
cons..
[0181] The assumption that .di-elect cons. has a Gaussian
distribution with mean 0 and variance .delta..sup.2 is the usual
basis of analysis that yields the equivalence of the maximum
likelihood estimates (MLE) for .beta..sub.0 and .beta..sub.1 the
least squares estimates. Although the above model has served well
to capture the behavior of many reliability characterizations, it
is worth noting that the assumptions do not have to be restricted
to those given above. The problem may be generalized to
g.sub.S(S)=.beta..sub.0+.beta..sub.1g.sub.A(a)+.di-elect cons.,
(3)
where g.sub.S() and g.sub.A() are increasing functions with
inverses g.sub.S.sup.-1 and g.sub.A.sup.-1, and .di-elect cons. is
a zero mean random variable with probability density function
f.sub.S(). With this formulation it is assumed that neither of the
functions g.sub.S() and g.sub.A() have parameters in need of
estimation from the data. Estimates .beta..sub.0, .beta..sub.1 and
any additional parameters defining f.sub.S() can be estimated from
n data pairs {(a.sub.i,s.sub.i),i=1, . . . , n} of flaws sizes and
signals by the maximum likelihood (ML) method. That is, let .theta.
be the vector of parameters needed to fully specify the density
f.sub.S(), then the ML estimates are given by {circumflex over
(.beta.)}.sub.0, {circumflex over (.beta.)}.sub.1, and {circumflex
over (.theta.)} which are the solutions to
max .beta. 0 , .beta. 1 , .theta. i = 1 n { f S ( g S ( s i ) - [
.beta. 0 + .beta. 1 g A ( a ) ] ) } ##EQU00001##
[0182] Letting x=g.sub.A(a) and y=g.sub.S(S) and f.sub.S(x)=(
{square root over (2.pi.)}).sup.-1e.sup.x.sup.2.sup./2 then
equation (3) is the usual linear regression with Gaussian
assumption for the errors given as
y=.beta..sub.0+.beta..sub.1x+.di-elect cons.. (4)
In this case the ML estimates and the least square linear
regression give the same estimates.
[0183] Equation (4) is written as a function of the signal having a
mean depending on the flaw size with probabilistic variation around
that mean. In designing POD quantification experiments the flaw
sizes may be set and signals measured corresponding to those
individual flaws. It is thus appropriate for the error structure in
the model to be associated with the signal measurement. However,
once the relationship of signal to flaw size is estimated we can
ask the inverse question of what is the best guess for the flaw
size that corresponds to a measured signal not included in the
original data set.
[0184] There are two general approaches to estimating the flaw size
corresponding to a subsequently measured signal, s', following the
"calibration" step. The classical approach is to simply solve the
mean equation in terms of the flaw size. That is, set
x ' = y ' - .beta. ^ 0 .beta. ^ 1 . ##EQU00002##
This is equivalent to reading of the flaw value corresponding to a
given signal from the mean line previously fitting the signal
relationship to the flaw size. The second approach is to recast the
problem as if the regression was for determining the flaw size
while treating the signal as the independent variable. That is,
find the coefficients for the regression
x=.gamma..sub.0+.gamma..sub.1y+.di-elect cons.. (5)
and then set It should be noted that these two approaches are not
equivalent. That is {circumflex over
(.gamma.)}.sub.1.noteq.1/{circumflex over (.beta.)}.sub.1, nor is
.noteq.
[0185] We will follow the classical approach as it also provides
the maximum likelihood estimate for the unknown flaw size in the
Gaussian models. To see this we consider the set of n data pairs
{(x.sub.i,y.sub.i),i=1, . . . , n} of flaws sizes and signals (or
appropriate functions of them) as well as one additional signal
y.sub.n+1 y.sub.n+1 for which the corresponding flaw size,
x.sub.n+1, is unknown. Using the regression model (4) and making
the Gaussian assumption that .di-elect
cons..apprxeq.N(0,.sigma..sup.2) we write the likelihood function
as
L = i = 1 n + 1 1 2 .pi. .sigma. 2 exp ( - 1 2 .sigma. 2 ( y i -
.beta. 0 - .beta. 1 x i ) 2 ) ##EQU00003##
Hence the log likelihood is given by
LL ( .beta. 0 , .beta. 1 , .sigma. , x n + 1 ) = - n + 1 2 ln ( 2
.pi. ) - ( n + 1 ) ln ( .sigma. ) - 1 2 .sigma. 2 i = 1 n + 1 ( y i
- .beta. 0 - .beta. 1 x i ) 2 ( 6 ) ##EQU00004##
The parameters that the log likelihood would be maximized over to
give an MLE are shown in the argument list.
[0186] Initially we will treat x.sub.n+1 as known, as in that case
we know the maximum likelihood estimates for the other parameters.
Specifically they are given in terms of the following
statistics.
S xy = 1 n + 1 i = 1 n + 1 ( x i - X _ ) ( y i - Y _ ) , S x 2 = 1
n + 1 i = 1 n + 1 ( x i - X _ ) 2 , and ##EQU00005## S y 2 = 1 n +
1 i = 1 n + 1 ( y i - Y _ ) 2 ##EQU00005.2##
where and X and Y are the means of the x and y data. We also
write
r = S xy S x S y . ##EQU00006##
It is well known that the maximum likelihood estimates are given
by
.beta. ^ 1 = S y S x r = S xy S x 2 , .beta. ^ 0 = Y _ - .beta. ^ 1
X _ , and .sigma. ^ 2 = 1 n + 1 i = 1 n + 1 ( y i - .beta. ^ 0 -
.beta. ^ 1 x i ) 2 ##EQU00007##
[0187] Making the substitutions back into equation (6) we see
that
LL = - n + 1 2 ln ( 2 .pi. ) - ( n + 1 ) ln ( .sigma. ^ ) - n + 1 2
##EQU00008##
and that the log likelihood is maximized when the estimated
variance is minimized. Thus, assuming that x.sub.n+1 is known we
know that the parameter estimates that maximizes the likelihood
also minimizes {circumflex over (.sigma.)}.sup.2. Note that
.sigma. ^ 2 = n n + 1 ( 1 n i = 1 n ( y i - .beta. ^ 0 - .beta. ^ 1
x i ) 2 ) + ( y n + 1 - .beta. ^ 0 - .beta. ^ 1 x n + 1 ) 2 n + 1
##EQU00009##
[0188] We know that the first term on the right hand side is
minimized by setting {circumflex over (.beta.)}.sub.0 and to the
solutions based on the first n data points. The contribution of the
x.sub.n+1 term can be made 0 by letting
x ^ n + 1 = y n + 1 - .beta. ^ 0 .beta. ^ 1 . ##EQU00010##
[0189] Not done here, but with a little algebra it can be shown
that the MLE estimates for the parameters based on the n+1 data
pairs
{ ( x i , y i ) , i = 1 , , n , ( y n + 1 - .beta. ^ 0 .beta. ^ 1 ,
y n + 1 ) } ##EQU00011##
are the same as those based on just the first n data pairs, where
the flaw sizes are all known.
[0190] It should be noted that not only is the estimator for
x.sub.n+1 given by
y n + 1 - .beta. 0 .beta. 1 ##EQU00012##
not unbiased, that in fact the distribution does not have a finite
variance nor mean. Thus the usual method of confidence intervals
using the estimated value plus or minus an appropriate multiple of
the standard error cannot be applied. There are several approaches
one can take to provide reasonable confidence limits for the
unknown flaw size characteristic, x.sub.n+1. One that we develop
here is to write out the distribution theory for y.sub.n+1 as if
x.sub.n+1 is known and y.sub.n+1 is a random variable. Developing
this theory we can write the confidence statement that holds for
y.sub.n+1 which is a function of x.sub.n+1, but instead of
constructing the interval for the signal we treat the signal as
given and instead write the interval in terms of the x.sub.n+1.
[0191] Here we give the results utilizing the following terms:
The percentile of the t distribution with n-2 degrees of freedom
and upper tail probability of .alpha./2 is given by t.sub.a/2:n-2
The mean of the signal data is
Y _ = 1 n i = 1 n y i ##EQU00013##
The mean of the flaw data is
X _ = 1 n i = 1 n x i . .beta. ^ 1 = i = 1 n ( x i - X _ ) ( y i -
Y _ ) i = 1 n ( x i - X _ ) 2 , ##EQU00014##
and {circumflex over (.beta.)}.sub.0= Y-{circumflex over
(.beta.)}.sub.1 X
.sigma. ^ 2 = i = 1 n ( y i - .beta. ^ 0 - .beta. ^ 1 x i ) 2 n - 2
##EQU00015##
is the unbiased estimate for the variance and
a = .beta. ^ 1 2 - .sigma. ^ 2 t .alpha. / 2 ; n - 2 2 i = 1 n ( x
i - X _ ) 2 ##EQU00016##
[0192] Using the above values determined from a "calibration" run
the following procedure is used to estimate the flaw size from an
unknown flaw size producing an observed signal of y.sub.n+1
1. Estimate of x.sub.n+1 is given by
x ^ n + 1 = y n + 1 - .beta. ^ 0 .beta. ^ 1 = X _ + y n + 1 - Y _
.beta. ^ 1 ##EQU00017##
2. To obtain a 1-.alpha..sup.1-.alpha. confidence limit for
x.sub.n+1 have to assure that the 1-.alpha. confidence limit for
.beta..sub.1 does not include 0. Thus test the hypothesis, HO:
.beta..sub.1=0 versus the alternative, Ha: .beta..sub.1.noteq.0
with a size .alpha. test. That is reject HO if and only if
.beta. ^ 1 2 i = 1 n ( x i - X _ ) 2 .sigma. ^ 2 .gtoreq. t .alpha.
/ 2 ; n - 2 2 ##EQU00018##
3. If HO is not rejected then the confidence interval for x.sub.n+1
would be infinite as there is not enough evidence that a dependency
between signal and flaw size exists. 4. A rejection of the null
hypothesis in step 2 assures the existence of a finite
100(1-.alpha.)% confidence interval for x.sub.n+1
Lower = X _ + .beta. ^ 1 ( y n + 1 - Y _ ) a - .sigma. ^ t .alpha.
/ 2 ; n - 2 a a ( n + 1 n ) + ( y n + 1 - Y _ ) 2 i = 1 n ( x i - X
_ ) 2 ##EQU00019## and ##EQU00019.2## Upper = X _ + .beta. ^ 1 ( y
n + 1 - Y _ ) a + .sigma. ^ t .alpha. / 2 ; n - 2 a a ( n + 1 n ) +
( y n + 1 - Y _ ) 2 i = 1 n ( x i - X _ ) 2 ##EQU00019.3##
The endpoints constituting the interval are not necessarily
equidistant from the point estimate.
[0193] Having thus described several aspects of at least one
embodiment of this invention, it is to be appreciated that various
alterations, modifications, and improvements will readily occur to
those skilled in the art.
[0194] Such alterations, modifications, and improvements are
intended to be part of this disclosure, and are intended to be
within the spirit and scope of the invention. Accordingly, the
foregoing description and drawings are by way of example only.
[0195] The above-described embodiments of the present invention can
be implemented in any of numerous ways. For example, the
embodiments may be implemented using hardware, software or a
combination thereof. When implemented in software, the software
code can be executed on any suitable processor or collection of
processors, whether provided in a single computer or distributed
among multiple computers.
[0196] Further, it should be appreciated that a computer may be
embodied in any of a number of forms, such as a rack-mounted
computer, a desktop computer, a laptop computer, or a tablet
computer. Additionally, a computer may be embedded in a device not
generally regarded as a computer but with suitable processing
capabilities, including a Personal Digital Assistant (PDA), a smart
phone or any other suitable portable or fixed electronic
device.
[0197] Also, a computer may have one or more input and output
devices. These devices can be used, among other things, to present
a user interface. Examples of output devices that can be used to
provide a user interface include printers or display screens for
visual presentation of output and speakers or other sound
generating devices for audible presentation of output. Examples of
input devices that can be used for a user interface include
keyboards, and pointing devices, such as mice, touch pads, and
digitizing tablets. As another example, a computer may receive
input information through speech recognition or in other audible
format.
[0198] Such computers may be interconnected by one or more networks
in any suitable form, including as a local area network or a wide
area network, such as an enterprise network or the Internet. Such
networks may be based on any suitable technology and may operate
according to any suitable protocol and may include wireless
networks, wired networks or fiber optic networks.
[0199] Also, the various methods or processes outlined herein may
be coded as software that is executable on one or more processors
that employ any one of a variety of operating systems or platforms.
Additionally, such software may be written using any of a number of
suitable programming languages and/or programming or scripting
tools, and also may be compiled as executable machine language code
or intermediate code that is executed on a framework or virtual
machine.
[0200] In this respect, the invention may be embodied as a computer
readable medium (or multiple computer readable media) (e.g., a
computer memory, one or more floppy discs, compact discs, optical
discs, magnetic tapes, flash memories, circuit configurations in
Field Programmable Gate Arrays or other semiconductor devices, or
other tangible computer storage medium) encoded with one or more
programs that, when executed on one or more computers or other
processors, perform methods that implement the various embodiments
of the invention discussed above. The computer readable medium or
media can be transportable, such that the program or programs
stored thereon can be loaded onto one or more different computers
or other processors to implement various aspects of the present
invention as discussed above.
[0201] In this respect, it should be appreciated that one
implementation of the above-described embodiments comprises at
least one computer-readable medium encoded with a computer program
(e.g., a plurality of instructions), which, when executed on a
processor, performs some or all of the above-discussed functions of
these embodiments. As used herein, the term "computer-readable
medium" encompasses only a computer-readable medium that can be
considered to be a machine or a manufacture (i.e., article of
manufacture). A computer-readable medium may be, for example, a
tangible medium on which computer-readable information may be
encoded or stored, a storage medium on which computer-readable
information may be encoded or stored, and/or a non-transitory
medium on which computer-readable information may be encoded or
stored. Other non-exhaustive examples of computer-readable media
include a computer memory (e.g., a ROM, a RAM, a flash memory, or
other type of computer memory), a magnetic disc or tape, an optical
disc, and/or other types of computer-readable media that can be
considered to be a machine or a manufacture.
[0202] The terms "program" or "software" are used herein in a
generic sense to refer to any type of computer code or set of
computer-executable instructions that can be employed to program a
computer or other processor to implement various aspects of the
present invention as discussed above. Additionally, it should be
appreciated that according to one aspect of this embodiment, one or
more computer programs that when executed perform methods of the
present invention need not reside on a single computer or
processor, but may be distributed in a modular fashion amongst a
number of different computers or processors to implement various
aspects of the present invention.
[0203] Computer-executable instructions may be in many forms, such
as program modules, executed by one or more computers or other
devices. Generally, program modules include routines, programs,
objects, components, data structures, etc. that perform particular
tasks or implement particular abstract data types. Typically the
functionality of the program modules may be combined or distributed
as desired in various embodiments.
[0204] Also, data structures may be stored in computer-readable
media in any suitable form. For simplicity of illustration, data
structures may be shown to have fields that are related through
location in the data structure. Such relationships may likewise be
achieved by assigning storage for the fields with locations in a
computer-readable medium that conveys relationship between the
fields. However, any suitable mechanism may be used to establish a
relationship between information in fields of a data structure,
including through the use of pointers, tags or other mechanisms
that establish relationship between data elements.
[0205] Various aspects of the present invention may be used alone,
in combination, or in a variety of arrangements not specifically
discussed in the embodiments described in the foregoing and is
therefore not limited in its application to the details and
arrangement of components set forth in the foregoing description or
illustrated in the drawings. For example, aspects described in one
embodiment may be combined in any manner with aspects described in
other embodiments.
[0206] Also, the invention may be embodied as a method, of which an
example has been provided. The acts performed as part of the method
may be ordered in any suitable way. Accordingly, embodiments may be
constructed in which acts are performed in an order different than
illustrated, which may include performing some acts simultaneously,
even though shown as sequential acts in illustrative
embodiments.
[0207] Use of ordinal terms such as "first," "second," "third,"
etc., in the claims to modify a claim element does not by itself
connote any priority, precedence, or order of one claim element
over another or the temporal order in which acts of a method are
performed, but are used merely as labels to distinguish one claim
element having a certain name from another element having a same
name (but for use of the ordinal term) to distinguish the claim
elements.
[0208] Embodiments described herein are related to the Application
filed concurrently herewith, bearing Attorney Docket Number
1884.2073-001, entitled "Component Adaptive Life Management," of
Goldfine et al., the entire disclosure of which is hereby
incorporated herein by reference.
[0209] Also, the phraseology and terminology used herein is for the
purpose of description and should not be regarded as limiting. The
use of "including," "comprising," or "having," "containing,"
"involving," and variations thereof herein, is meant to encompass
the items listed thereafter and equivalents thereof as well as
additional items.
* * * * *