U.S. patent application number 15/203470 was filed with the patent office on 2016-10-27 for maintenance systems and methods for use in analyzing maintenance data.
The applicant listed for this patent is The Boeing Company. Invention is credited to Steven David Chapman, Peter J. Lake, Jay Kevin McCullough.
Application Number | 20160314628 15/203470 |
Document ID | / |
Family ID | 52428393 |
Filed Date | 2016-10-27 |
United States Patent
Application |
20160314628 |
Kind Code |
A1 |
Chapman; Steven David ; et
al. |
October 27, 2016 |
MAINTENANCE SYSTEMS AND METHODS FOR USE IN ANALYZING MAINTENANCE
DATA
Abstract
Methods and maintenance systems for use in analyzing data
related to maintenance of at least one vehicle are disclosed. One
example method includes retrieving, by a computing device, a
plurality of diagnostic entries associated with at least one fault
message from a database of diagnostic entries, each diagnostic
entry including an identified corrective action and a date on which
the identified corrective action was taken; identifying a plurality
of groups of diagnostic entries, wherein the diagnostic entries in
a group have a same corrective action, and each group has a
confidence level associated with its corrective action; and
weighting the confidence level for each group based on an age of
the plurality of diagnostic entries in the group.
Inventors: |
Chapman; Steven David;
(O'Fallon, MO) ; Lake; Peter J.; (Auburn, WA)
; McCullough; Jay Kevin; (Belleville, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Boeing Company |
Chicago |
IL |
US |
|
|
Family ID: |
52428393 |
Appl. No.: |
15/203470 |
Filed: |
July 6, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13959425 |
Aug 5, 2013 |
9396592 |
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15203470 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G07C 5/085 20130101;
G06F 16/24578 20190101; G07C 5/0808 20130101; G06F 16/248 20190101;
G07C 5/008 20130101 |
International
Class: |
G07C 5/00 20060101
G07C005/00; G07C 5/08 20060101 G07C005/08; G06F 17/30 20060101
G06F017/30 |
Claims
1-20. (canceled)
21. A method for use in analyzing data related to maintenance of at
least one vehicle, said method comprising: retrieving, by a
computing device, a plurality of diagnostic entries associated with
at least one fault message from a database of diagnostic entries,
each diagnostic entry including an identified corrective action and
a date on which the identified corrective action was taken;
identifying a plurality of groups of diagnostic entries, wherein
the diagnostic entries in a group have a same corrective action;
weighting a confidence level associated with each group's
corrective action based on an age of the plurality of diagnostic
entries in the group using a decay function; determining a
suggested corrective action based at least in part on the weighted
confidence levels associated with the corrective actions; and
displaying, to a user, the suggested corrective action to
facilitate the user remedying the at least one fault message by
performing the suggested corrective action.
22. The method of claim 21, wherein weighting the confidence level
associated with each group's corrective action based on an age of
the plurality of diagnostic entries in the group using a decay
function comprises weighting the confidence level associated with
each group's corrective action based on an average age of the
plurality of diagnostic entries in the group using a decay
function.
23. The method of claim 22, wherein weighting the confidence level
associated with each group's corrective action based on an average
age of the plurality of diagnostic entries in the group using a
decay function comprises weighting the confidence level associated
with each group's corrective action based on an average age of the
plurality of diagnostic entries in the group using a linear decay
function.
24. The method of claim 22, wherein weighting the confidence level
associated with each group's corrective action based on an average
age of the plurality of diagnostic entries in the group using a
decay function comprises weighting the confidence level associated
with each group's corrective action based on an average age of the
plurality of diagnostic entries in the group using a Gaussian
function.
25. The method of claim 24, wherein weighting the confidence level
associated with each group's corrective action based on an average
age of the plurality of diagnostic entries in the group using a
Gaussian function comprises weighting the confidence level
associated with each group's corrective action based on an average
age of the plurality of diagnostic entries in the group to produce
a weighted confidence level by multiplying the confidence level for
the group by a weighting factor determined for that group by f ( x
) = a - ( x - b ) 2 2 c 2 ##EQU00005## where "f(x)" is the
weighting factor, "x" is the average age, "a" is the maximum value
of the weighting factor, "b" is the age in years at which to apply
the maximum value of the weighting factor, "c" controls how quickly
the weight decreases as age increases, and "e" is Euler's
number.
26. The method of claim 25, wherein "a" has a value of 21 and "b"
has a value of 0.
27. The method of claim 26, wherein "c" has a value of about
4.25.
28. The method of claim 21, further comprising determining a
confidence indicator for each group as a product of the weighted
confidence level for the group and a number of diagnostic entries
in the group.
29. A maintenance system for use in analyzing data related to
maintenance of at least one vehicle, said maintenance system
comprising: a display device; a memory device storing a plurality
of diagnostic entries, each diagnostic entry including at least one
fault message, an identified corrective action, and a date; and a
processor coupled to said memory device and said display device,
said processor configured to: determine a weighting factor for each
group of a plurality of groups of diagnostic entries based on an
age of the diagnostic entries in the group using a decay function,
wherein the diagnostic entries in a group have a same corrective
action; apply the weighting factor for each group to a confidence
level associated with the corrective action of the group to
determine a weighted confidence level for the corrective action;
determine a suggested corrective action based at least in part on
the weighted confidence levels for the corrective actions; and
display, on the display device, the suggested corrective action to
facilitate a user remedying the at least one fault message by
performing the suggested corrective action.
30. The maintenance system of claim 29, wherein said processor is
configured to determine the weighting factor for each group of the
plurality of groups of diagnostic entries based on an average age
of the diagnostic entries in the group.
31. The maintenance system of claim 30, wherein said processor is
configured to determine the weighting factor for each group of the
plurality of groups of diagnostic entries based on an average age
of the diagnostic entries in the group using a linear decay
function.
32. The maintenance system of claim 30, wherein said processor is
configured to determine the weighting factor for each group of the
plurality of groups of diagnostic entries based on an average age
of the diagnostic entries in the group using a Gaussian
function.
33. The maintenance system of claim 32, wherein said processor is
configured to determine the weighting factor for each group by f (
x ) = a - ( x - b ) 2 2 c 2 ##EQU00006## where "f(x)" is the
weighting factor, "x" is the average age, "a" is the maximum value
of the weighting factor, "b" is the age in years at which to apply
the maximum value of the weighting factor, "c" controls how quickly
the weight decreases as age increases, and "e" is Euler's
number.
34. The maintenance system of claim 29, wherein the vehicle is an
aircraft.
35. The maintenance system of claim 29, further comprising
determining a confidence indicator for each groups corrective
action as a product of the weighted confidence level for the
group's corrective action and a number of diagnostic entries in the
group.
36. One or more non-transitory computer-readable storage media
having computer-executable instructions embodied thereon, wherein
when executed by at least one processor, the computer-executable
instructions cause the processor to: determine a weighting factor
for each group of a plurality of groups of diagnostic entries based
on an age of the diagnostic entries in the group using a decay
function, wherein the diagnostic entries in a group have a same
corrective action; determine a confidence level associated with the
corrective action of each group of diagnostic entries; apply the
weighting factor for each group to the confidence level associated
with the corrective action of the group to determine a weighted
confidence level for the corrective action; determine a suggested
corrective action based at least in part on the weighted confidence
levels for the corrective actions; and display, on a display
device, the suggested corrective action to facilitate a user
remedying the at least one fault message by performing the
suggested corrective action.
37. The one or more non-transitory computer-readable storage media
of claim 36, wherein when executed by the at least one processor,
the computer-executable instructions further cause the processor to
determine the weighting factor for each group of the plurality of
groups of diagnostic entries based on an average age of the
diagnostic entries in the group.
38. The one or more non-transitory computer-readable storage media
of claim 37, wherein when executed by the at least one processor,
the computer-executable instructions further cause the processor to
determine the weighting factor for each group of the plurality of
groups of diagnostic entries based on an average age of the
diagnostic entries in the group using a linear decay function.
39. The one or more non-transitory computer-readable storage media
of claim 37, wherein when executed by the at least one processor,
the computer-executable instructions further cause the processor to
determine the weighting factor for each group of the plurality of
groups of diagnostic entries based on an average age of the
diagnostic entries in the group using a Gaussian function.
40. The one or more non-transitory computer-readable storage media
of claim 39, wherein when executed by the at least one processor,
the computer-executable instructions further cause the processor to
determine the weighting factor for each group by f ( x ) = a - ( x
- b ) 2 2 c 2 ##EQU00007## where "f(x)" is the weighting factor,
"x" is the average age, "a" is the maximum value of the weighting
factor, "b" is the age in years at which to apply the maximum value
of the weighting factor, "c" controls how quickly the weight
decreases as age increases, and "e" is Euler's number.
Description
BACKGROUND
[0001] The field of the disclosure relates generally to maintenance
systems and methods and, more particularly, to analyzing data
related to maintenance of at least one vehicle.
[0002] Various types of vehicles are known to include monitoring
technologies, which enable vehicles to detect abnormal conditions
and report the abnormal conditions as fault messages associated
with one or more components of the vehicle. Aircraft, for example,
often include systems to monitor components of the aircraft during
flight and report fault messages when abnormal conditions are
identified. Fault messages may be reported to an operator of the
aircraft and/or to another system or individual to ensure
appropriate maintenance is pursued to address the abnormal
conditions underlying the fault messages. Additionally, yet
separately, during maintenance of a vehicle, reports are often
generated, which indicate action codes and/or text descriptions of
the repair and/or other maintenance procedures performed by the
maintenance personnel on the vehicle.
BRIEF DESCRIPTION
[0003] In one aspect, a method for use in analyzing data related to
maintenance of at least one vehicle is described. The method
includes retrieving, by a computing device, a plurality of
diagnostic entries associated with at least one fault message from
a database of diagnostic entries. Each diagnostic entry includes an
identified corrective action and a date on which the identified
corrective action was taken. The method includes identifying a
plurality of groups of diagnostic entries. All diagnostic entries
in a group have a same corrective action. The method includes
weighting a confidence level associated with the corrective action
for at least one group based on an age of the plurality of
diagnostic entries in the group.
[0004] In another aspect, a maintenance system for use in analyzing
data related to maintenance of at least one vehicle includes a
memory device storing a plurality of diagnostic entries, and a
processor coupled to the memory device. Each diagnostic entry
includes at least one fault message, an identified corrective
action, and a date. The processor is configured to identify a
plurality of groups of diagnostic entries from the plurality of
diagnostic entries, and determine a plurality of weighting factors
for the groups based on an age of the plurality of diagnostic
entries in the groups. All diagnostic entries in a group have a
same corrective action.
[0005] In yet another aspect, one or more non-transitory
computer-readable storage media having computer-executable
instructions embodied thereon are described. When executed by at
least one processor, the computer-executable instructions cause the
processor to identify a plurality of groups of diagnostic entries
from a plurality of diagnostic entries. Each diagnostic entry
includes at least one fault message, an identified corrective
action, and a date, and all diagnostic entries in a group have a
same corrective action. The computer-executable instructions cause
the processor to determine a weighting factor for each group based
on an age of the plurality of diagnostic entries in the group.
[0006] The features, functions, and advantages that have been
discussed can be achieved independently in various embodiments or
may be combined in yet other embodiments further details of which
can be seen with reference to the following description and
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a block diagram of an exemplary computing
device.
[0008] FIG. 2 is a block diagram of an exemplary maintenance
system.
[0009] FIG. 3 is a graph of a weighting factor as a function of
time for use with the system shown in FIG. 2.
[0010] FIG. 4 is an example display of suggested corrective actions
returned by the system show in FIG. 2 for a particular maintenance
code.
DETAILED DESCRIPTION
[0011] The subject matter described herein relates to analyzing
data related to maintenance of a vehicle to provide a diagnostic
entry, which includes a fault message and a corrective action
associated with the fault message.
[0012] In one embodiment, technical effects of the methods,
systems, and computer-readable media described herein include at
least one of: (a) identifying a plurality of groups of diagnostic
entries from a plurality of diagnostic entries, and (b) determining
a weighting factor for each group based on an age of the plurality
of diagnostic entries in the group.
[0013] As used herein, an element or step recited in the singular
and proceeded with the word "a" or "an" should be understood as not
excluding plural elements or steps unless such exclusion is
explicitly recited. Furthermore, references to "one implementation"
of the present invention or the "exemplary implementation" are not
intended to be interpreted as excluding the existence of additional
implementations that also incorporate the recited features.
[0014] FIG. 1 is a block diagram of an exemplary computing device
100 that may be used to automatically generate a production
operating system. In the exemplary implementation, computing device
100 includes a memory 106 and a processor 104 that is coupled to
memory 106 for executing programmed instructions. Processor 104 may
include one or more processing units (e.g., in a multi-core
configuration). Computing device 100 is programmable to perform one
or more operations described herein by programming memory 106
and/or processor 104. For example, processor 104 may be programmed
by encoding an operation as one or more executable instructions and
providing the executable instructions in memory device 106.
[0015] Processor 104 may include, but is not limited to, a general
purpose central processing unit (CPU), a microcontroller, a reduced
instruction set computer (RISC) processor, an application specific
integrated circuit (ASIC), a programmable logic circuit (PLC),
and/or any other circuit or processor capable of executing the
functions described herein. The methods described herein may be
encoded as executable instructions embodied in a computer-readable
medium including, without limitation, a storage device and/or a
memory device. Such instructions, when executed by processor 104,
cause processor 104 to perform at least a portion of the methods
described herein. The above examples are exemplary only, and thus
are not intended to limit in any way the definition and/or meaning
of the term processor.
[0016] Memory device 106, as described herein, is one or more
devices that enable information such as executable instructions
and/or other data to be stored and retrieved. Memory device 106 may
include one or more computer-readable media, such as, without
limitation, dynamic random access memory (DRAM), static random
access memory (SRAM), a solid state disk, and/or a hard disk.
Memory device 106 may be configured to store, without limitation,
maintenance event log, diagnostic entries, fault messages, and/or
any other type of data suitable for use with the methods and
systems described herein.
[0017] In the exemplary implementation, computing device 100
includes a presentation interface 108 that is coupled to processor
104. Presentation interface 108 outputs (e.g., display, print,
and/or otherwise output) information such as, but not limited to,
installation data, configuration data, test data, error messages,
and/or any other type of data to a user 114. For example,
presentation interface 108 may include a display adapter (not shown
in FIG. 1) that is coupled to a display device, such as a cathode
ray tube (CRT), a liquid crystal display (LCD), a light-emitting
diode (LED) display, an organic LED (OLED) display, and/or an
"electronic ink" display. In some implementations, presentation
interface 108 includes more than one display device. In addition,
or in the alternative, presentation interface 108 may include a
printer.
[0018] In the exemplary implementation, computing device 100
includes an input interface 110 that receives input from user 114.
For example, input interface 110 may be configured to receive
selections, requests, credentials, and/or any other type of inputs
from user 114 suitable for use with the methods and systems
described herein. In the exemplary implementation, input interface
110 is coupled to processor 104 and may include, for example, a
keyboard, a card reader (e.g., a smartcard reader), a pointing
device, a mouse, a stylus, a touch sensitive panel (e.g., a touch
pad or a touch screen), a gyroscope, an accelerometer, a position
detector, and/or an audio input interface. A single component, such
as a touch screen, may function as both a display device of
presentation interface 108 and as input interface 110.
[0019] In the exemplary implementation, computing device 100
includes a communication interface 112 coupled to memory 106 and/or
processor 104. Communication interface 112 is coupled in
communication with a remote device, such as another computing
device 100. For example, communication interface 112 may include,
without limitation, a wired network adapter, a wireless network
adapter, and/or a mobile telecommunications adapter.
[0020] Instructions for operating systems and applications are
located in a functional form on non-transitory memory 106 for
execution by processor 104 to perform one or more of the processes
described herein. These instructions in the different
implementations may be embodied on different physical or tangible
computer-readable media, such as memory 106 or another memory, such
as a computer-readable media 118, which may include, without
limitation, a flash drive, CD-ROM, thumb drive, floppy disk, etc.
Further, instructions are located in a functional form on
non-transitory computer-readable media 118, which may include,
without limitation, a flash drive, CD-ROM, thumb drive, floppy
disk, etc. Computer-readable media 118 is selectively insertable
and/or removable from computing device 100 to permit access and/or
execution by processor 104. In one example, computer-readable media
118 includes an optical or magnetic disc that is inserted or placed
into a CD/DVD drive or other device associated with memory 106
and/or processor 104. In some instances, computer-readable media
118 may not be removable.
[0021] FIG. 2 illustrates an exemplary maintenance system 200,
which includes a maintenance server 202 and a vehicle 204. While
vehicle 204 is illustrated and referred to herein as an aircraft,
it should be appreciated that other types of vehicles may be
included in the other maintenance system implementations. As shown,
maintenance server 202 is coupled to aircraft 204 through a network
206. Network 206 may include, without limitation, the Internet, an
intranet, a local area network (LAN), a wide area network (WAN), a
mobile network, a virtual network, and/or another suitable network
for communicating data between maintenance server 202, aircraft
204, and/or other computing devices. In use, aircraft 204 transmits
one or more maintenance related messages, such as, for example,
fault messages, to maintenance server 202 through network 206. In
turn, maintenance server 202 receives the one or more maintenance
related messages from aircraft 204.
[0022] Additionally, or alternatively, maintenance system 200
includes a fault server 208 coupled to maintenance server 202
through network 206. As shown in FIG. 2, fault server 208 may be
coupled directly to aircraft 204 and/or coupled to aircraft 204
through network 206. Fault server 208 is configured to receive at
least fault messages from aircraft 204 and transmit such messages
to maintenance server 202. Fault server 208 may be configured to
provide messages between maintenance server 202 and aircraft 204
for a variety of reasons, including for example, when maintenance
server 202 has limited or no direct communication with aircraft
204.
[0023] Maintenance system 200 includes a repair identification
server 210 coupled to maintenance server 202 and fault server 208
through network 206. Repair identification server 210 correlates
fault message from server 208 with one or more appropriate
maintenance event logs from server 210. Repair identification
server 210 also provides suggested corrective actions for fault
messages based on the correlated fault messages and maintenance
event logs.
[0024] In the exemplary implementation, maintenance server 202,
fault servers 208, and repair identification server 210 are
examples of computing devices 100. It should be understood that, in
various implementations, each of maintenance server 202, fault
servers 208, and repair identification server 210 may include
multiple computing devices 100. In one example, maintenance server
202 includes a first computing device 100 located proximate at a
maintenance site for use by maintenance personnel and a second
computing device 100 located remote from the maintenance site for
use by a data analyzer. In at least one example, maintenance server
202, fault servers 208, and repair identification server 210 each
include a single computing device 100.
[0025] In the exemplary implementation, during operation, one or
more fault messages are generated by aircraft 204. Fault messages
may include any message generated indicating a failure, an error, a
malfunction, or other abnormal condition of a component, a system,
and/or operation of aircraft 204. In one example, aircraft 204
generated a fault message, indicating a bleed fan error associated
with a modulation valve. In the exemplary implementation, the fault
message is transmitted from aircraft 204 to fault server 208
through network 206. In turn, fault server 208 receives the fault
message and stores the fault message in memory device 106. The
fault message may be transmitted from aircraft 204 to fault server
208, while aircraft 204 is in-flight or when aircraft 204 is
grounded. Fault server 208 subsequently transmits the fault message
to maintenance server 202, which receives the fault messages and
stores the fault message in memory 106. Alternatively, in other
implementations, aircraft 204 transmits the fault message directly
to maintenance server 202, which receives the fault messages and
stores the fault message in memory 106.
[0026] In response to a fault message, aircraft 204 may be
subjected to one or more maintenance sessions. Depending on the
type and/or the severity of the fault message, the maintenance
session may be scheduled immediately or at a convenient time.
During a maintenance session, maintenance is performed on aircraft
204 to identify and/or remedy a cause of the fault message. In
various examples, one or more components may be subjected to a
corrective action, such as, without limitation, removing,
replacing, swapping, repairing, checking, and/or deferring, etc.
User 114 associated with maintenance of aircraft 204 generates
maintenance event logs, which are received by maintenance server
202 from user 114 and stored in maintenance server 202. Maintenance
event log may include one or more maintenance events, which may
include, without limitation, individual, single, or multiple
corrective actions, part numbers, part names, test results, check
results, and/or descriptions, etc.
[0027] It should be appreciated that aircraft 204 may be subjected
to multiple maintenance sessions in response to one or more fault
messages. Accordingly, maintenance event logs may include multiple
maintenance events associated with one or more fault messages.
[0028] Repair identification server 210 correlates each fault
message to one or more appropriate maintenance event logs. In the
exemplary implementation, processor 104 correlates the fault
message and the maintenance event log, potentially based on
identification of aircraft 204, location of the aircraft 204,
date/time data included in the fault messages and the maintenance
event log, and/or descriptions included the fault messages and the
maintenance event log. Repair identification server 210 analyzes
the maintenance event logs to identify the corrective actions taken
during the maintenance event in response to the correlated fault
message. A confidence factor is calculated for each corrective
action identified. The confidence factor represents the likelihood
that the corrective action that the repair identification server
210 identified in the maintenance log and associated with a fault
message is the actual corrective action that was performed in
response to the fault message.
[0029] The data collected by maintenance system 200 may be used to
identify suggested corrective actions for fault messages. Thus, for
example, the system 200 (and more particularly repair
identification server 210) may retrieve the corrective action(s)
identified in one or more maintenance events associated with a
particular fault message and display the corrective actions to a
user. Generally, the system 200 bases the suggested corrective
action(s) on the number of times the particular corrective action
has been taken in response to a particular fault message, and the
confidence factor. Generally, corrective actions that have been
taken more times are more likely to be correct. Corrective actions
with higher confidence factors are more likely to be the correct
action to take in response to a fault message. Additionally, more
recent maintenance events generally reflect the most recent
corrective action taken to address the associated fault message.
Other, older, corrective actions, identified in other maintenance
events, may be less instructive due to changes and modifications in
aircraft systems, component changes/upgrades/redesigns, etc. Thus,
processor 104 identifies suggested corrective actions based at
least in part on the age of the maintenance event associated with
the corrective action.
[0030] In the exemplary implementation, maintenance system 200 (and
particularly repair identification server 210) searches for and
retrieves all maintenance events associated with a particular fault
message and weights the data by age. More recent maintenance events
(and their associated corrective actions) are given more weight
than older maintenance events. In particular, each group of
incidents for each particular corrective action is identified and
the average age of the group is calculated. The confidence factor
for each group is adjusted by a weighting factor based on the
average age of the data in the group. In the exemplary
implementation, the weighting factor is determined by a Gaussian
function based on the average age of the corrective actions in the
group. In other implementations, a linear decline or a decay
function may be applied. In still other implementations, a cut-off
date may be applied in which maintenance events and corrective
actions having an age less than the cut-off date are not modified
and those with an average age greater than or equal to the cut-off
date are ignored or have their confidence levels set to zero.
[0031] The Gaussian function produces a weighting factor that
gradually decreases the relevance of data for a short time (e.g.,
one to two years) and decreases it more rapidly thereafter (but
without reaching zero). The exemplary Gaussian function is:
f ( x ) = a - ( x - b ) 2 2 c 2 ( 1 ) ##EQU00001##
where "f(x)" is the weighting factor, "x" is age in years of the
corrective action(s), "a" is the maximum weight to apply, "b" is
the age in years at which to apply the maximum weight, "c" controls
how quickly the weight decreases as age increases, and "e" is
Euler's number. In the exemplary implementation, a corrective
action that is new is assigned a weight of 100%. Accordingly "a" is
assigned a value of 1 and the age at which to apply the maximum
(i.e., "b") is zero. This reduces equation (1) to:
f ( x ) = - x 2 2 c 2 ( 2 ) ##EQU00002##
[0032] To determine how long it will take for the weight applied to
a corrective action to approach zero as age increases, a value for
"c" is determined. In the exemplary implementation, a half-life for
corrective actions was determined to be five years. That is, the
weight applied to a corrective action reaches 0.5 when the data is
five years old. In other implementations, other suitable values may
be selected. For example, for a component that is frequently
changed, redesigned, etc., a shorter half-life may be selected;
while components that seldom change may be subjected to a greater
half-life. Moreover, the half-life may be varied based on the type
of vehicle. Thus, data for maritime vessels, commercial aircraft,
and military vehicles may all utilize a different half-life. To
determine the value for "c", a weight of 0.5 and an age of 5 years
are inserted into equation (2) resulting in:
0.5 - - 5 2 2 c 2 ( 3 ) ##EQU00003##
Solving equation (3) for "c" yields a value of 4.24660900144. Thus,
the final weighting equation is:
f ( x ) = - x 2 36.0673750222 ( 4 ) ##EQU00004##
FIG. 3 is a graph of the function in equation (4) as a function of
age in years.
[0033] FIG. 4 is an example display 400 of suggested corrective
actions returned by system 200 for a particular maintenance code.
The display 400 may be provided to user 114 of maintenance server
202, such as via presentation interface 108. In this example, six
difference corrective actions are listed in a corrective action
column 402. A count column 404 lists the number of times that the
particular corrective action was used, and a confidence column 406
indicates the confidence that the corrective action solved the
problem. The confidence column 406 indicates the unweighted
confidence values for each corrective action. An age column 408
displays the average age in years for the events associated with
each corrective action. A score column 410 lists the confidence
factors from column 406 weighted according to equation (4). A
confidence indicator column 412 graphically presents an overall
confidence measurement for each of the corrective actions. In the
exemplary implementation, the overall confidence measurement is the
product of the weighted confidence factor from the score column 410
and the number of events (from column 404) associated with that
corrective action.
[0034] It should be appreciated that one or more aspects of the
present disclosure transform a general-purpose computing device
into a special-purpose computing device when configured to perform
the functions, methods, and/or processes described herein.
[0035] This written description uses examples to disclose various
implementations, which include the best mode, to enable any person
skilled in the art to practice those embodiments, including making
and using any devices or systems and performing any incorporated
methods. The patentable scope is defined by the claims, and may
include other examples that occur to those skilled in the art. Such
other examples are intended to be within the scope of the claims if
they have structural elements that do not differ from the literal
language of the claims, or if they include equivalent structural
elements with insubstantial differences from the literal languages
of the claims.
* * * * *