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WO2000060464A1 - Analyse de donnees de fonctionnement pour le diagnostic des incidents de fonctionnement des locomotives et systeme a cet effet - Google Patents

Analyse de donnees de fonctionnement pour le diagnostic des incidents de fonctionnement des locomotives et systeme a cet effet Download PDF

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Publication number
WO2000060464A1
WO2000060464A1 PCT/US2000/008661 US0008661W WO0060464A1 WO 2000060464 A1 WO2000060464 A1 WO 2000060464A1 US 0008661 W US0008661 W US 0008661W WO 0060464 A1 WO0060464 A1 WO 0060464A1
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WO
WIPO (PCT)
Prior art keywords
data
log data
fault log
equipment
fault
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2000/008661
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English (en)
Inventor
David Richard Gibson
Nicholas Edward Roddy
Anil Varma
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
General Electric Co
Original Assignee
General Electric Co
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US09/285,611 external-priority patent/US6343236B1/en
Priority claimed from US09/285,612 external-priority patent/US6415395B1/en
Priority claimed from US09/438,271 external-priority patent/US6336065B1/en
Priority claimed from US09/447,064 external-priority patent/US6622264B1/en
Application filed by General Electric Co filed Critical General Electric Co
Priority to CA002368818A priority Critical patent/CA2368818C/fr
Priority to MXPA01009957A priority patent/MXPA01009957A/es
Priority to AU40602/00A priority patent/AU758061B2/en
Publication of WO2000060464A1 publication Critical patent/WO2000060464A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
    • G06F11/2257—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing using expert systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61C—LOCOMOTIVES; MOTOR RAILCARS
    • B61C17/00—Arrangement or disposition of parts; Details or accessories not otherwise provided for; Use of control gear and control systems
    • B61C17/04—Arrangement or disposition of driving cabins, footplates or engine rooms; Ventilation thereof
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61C—LOCOMOTIVES; MOTOR RAILCARS
    • B61C17/00—Arrangement or disposition of parts; Details or accessories not otherwise provided for; Use of control gear and control systems
    • B61C17/12—Control gear; Arrangements for controlling locomotives from remote points in the train or when operating in multiple units
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
    • G06F11/2268—Logging of test results
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T30/00—Transportation of goods or passengers via railways, e.g. energy recovery or reducing air resistance

Definitions

  • the present invention relates generally to diagnostics of railroad locomotives and other large land-based, self-powered transport equipment, and, more specifically, to a system and method for hybrid processing of snapshot operational parameter data and fault log data to
  • a machine such as a locomotive or other complex systems used in industrial processes, medical imaging, telecommunications, aerospace applications, power generation, etc., includes elaborate controls and sensors that generate faults when anomalous operating conditions of
  • CBR Case Based Reasoning
  • a case refers to a problem/solution description pair that represents a diagnosis of a problem and an appropriate repair.
  • U.S. Patent Application Serial No. 09/285,611 (Attorney Docket No. RD-26576), assigned to the same assignee of the present invention, discloses a system and method for processing historical repair data and fault log data, which is not restricted to sequential occurrences of fault log entries and which provides weighted repair and distinct fault cluster combinations, to facilitate analysis of new fault log data from a malfunctioning machine. Further, U.S. Patent Application Serial No. 09/285,612, (Attorney Docket No.
  • the present invention fulfills the foregoing needs by providing a method for analyzing fault log data from railroad locomotives and other large land-based, self-powered transport equipment undergoing diagnostics.
  • a receiving step allows for receiving fault log data comprising a plurality of faults from the equipment.
  • Respective executing steps allow for executing a set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data, and for executing a set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the equipment.
  • the methods allows for receiving fault log data including a plurality of faults from the equipment and for receiving operational parameter data including a plurality of operational parameters from the equipment.
  • a executing step allows for executing a set of candidate snapshot anomalies upon the fault log data and upon the operational parameter data.
  • a combining step allows for combining any candidate snapshot anomalies respectively triggered by the fault log data with any candidate snapshot anomalies respectively triggered by the parameter data to generate data predictive of malfunctions of the equipment.
  • the present invention fulfills the foregoing needs by providing a process for analyzing fault log data from railroad locomotives and other large land-based, self-powered transport equipment to identify respective faults and/or fault combinations predictive of equipment malfunctions.
  • the process allows for downloading new fault log data from the equipment.
  • the process further allows for retrieving prior fault log data of the equipment.
  • the prior fault log data may be obtained during an earlier download relative to a present download of new fault log data.
  • a comparing step allows for comparing the new fault log data against the prior fault log data
  • an adjusting step allows for adjusting any repair recommendations for the earlier download of fault log data based upon the comparison of the new fault log data and the prior fault log data.
  • the foregoing process may be used for developing a fault analysis kit, either in electronic form suitable for computerized processing or otherwise, e.g., a check list, flowchart, instruction chart, software program, etc., that enables respective users to systematically and accurately analyze the fault log data from the equipment so as to be able to identify the respective faults and/or fault combinations which are predictive of malfunctions of the equipment.
  • the present invention further fulfills the foregoing needs by providing a system for analyzing fault log data and operational parameter data from railroad locomotives and other large land-based, self-powered transport equipment undergoing diagnostics.
  • the system includes a module for receiving fault log data including a plurality of faults from the equipment.
  • a memory unit is configured to store a set of noise-reduction filters and a set of candidate snapshot anomalies.
  • a processor is respectively coupled to the module for receiving fault log data and to the memory unit.
  • the processor includes processor means for executing the set of noise-reduction filters upon the received fault log data to generate noise- reduced fault log data, and further includes processor means for executing the set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the equipment.
  • FIG. 1 is one embodiment of a block diagram of a system of the present invention that uses a processor for processing operational parameter data and fault log data from railroad locomotives and other large land-based, self-powered transport equipment and diagnosing malfunctioning equipment;
  • FIG. 2 is an illustration of exemplary repair log data
  • FIG. 3 is an illustration of exemplary fault log data
  • FIG. 4 is an illustration of exemplary hybrid data including in part fault log data and operational parameter data and further including exemplary noise reduction filters and candidate snapshot anomalies used to analyze the hybrid data;
  • FIG. 5 is a flow chart illustrating one exemplary embodiment for using the noise reduction filters and the candidate snapshot anomalies;
  • FIG. 6 illustrates further details regarding the processor of
  • FIG. 1 A first figure.
  • FIG. 7 is a flowchart describing steps for selecting a respective repair for a predicted malfunction upon analysis of the fault data and/or the operational parameter data
  • FIG. 8 is flow chart describing steps for generating a plurality of respective cases, including predetermined repairs, fault cluster combinations and operational parameter observations for each case
  • FIG. 9 is a flowchart describing the steps for adding a new case to the case database and updating the weighted repair, distinct fault cluster combinations and respective weights for the candidate snapshot anomalies
  • FIG. 10 is a flow chart of an exemplary embodiment of the process of the present invention for analyzing fault log data so as to identify respective faults and/or fault combinations predictive of equipment malfunctions;
  • FIG. 11 is a flow chart illustrating further details in connection with the process of FIG. 10.
  • FIG. 12 is flow chart describing steps for generating a plurality of respective cases, including predetermined repairs, fault cluster combinations and operational parameter observations for each case.
  • FIG. 1 diagrammatically illustrates one embodiment of a diagnostic system 10 of the present invention.
  • System 10 provides a process for automatically harvesting or mining repair data comprising a plurality of related and unrelated repairs and fault log data comprising a plurality of faults, from one or more machines, such as railroad locomotives and other large land-based, self-powered transport equipment, and generating weighted repair and distinct fault cluster combinations which are diagnostically significant predictors to facilitate analysis of new fault log data from a malfunctioning locomotive.
  • system 10 allows for hybridly analyzing the fault log data jointly with operational parameters from the machine.
  • system 10 can be used in conjunction with any machine in which operation of the machine is monitored, such as a chemical, an electronic, a mechanical, or a microprocessor machine.
  • exemplary system 10 includes a processor 12 such as a computer (e.g., UNIX workstation) having a hard drive, input devices such as a keyboard, a mouse, magnetic storage media (e.g., tape cartridges or disks), optical storage media (e.g., CD-ROMs), and output devices such as a display and a printer.
  • Processor 12 is operably connected to and processes data contained in a repair data storage unit 20 and a fault log data storage unit 22.
  • Processor 12 is further respectively connected to and processes noise-reduction filters stored in a storage unit 27, and candidate snapshot anomalies stored in a storage unit 28.
  • Repair data storage unit 20 includes repair data or records regarding a plurality of related and unrelated repairs for one or more locomotives.
  • FIG. 2 shows an exemplary portion 30 of the repair data contained in repair data storage unit 20.
  • the repair data may include a customer identification number 32, a locomotive identification or unit number 33, the date 34 of the repair, the repair code 35, a repair code description 36, a description of the actual repair 37 performed, etc.
  • Fault log data storage unit 22 includes fault log data or records regarding a plurality of faults occurring prior to the repairs for the one or more locomotives.
  • FIG. 3 shows an exemplary portion 40 of the fault log data contained in fault log data storage unit 22.
  • the fault log data may include a customer identification number 42, a locomotive identification number or unit 44, the date 45 when the fault occurred, a fault code 46, a fault code description 48, etc.
  • additional data used in the analysis of the present invention include operational parameter data indicative of a plurality of operational parameters or operational conditions of the machine.
  • the operational parameter data may be obtained from various sensor readings or observations, e.g., temperature sensor readings, pressure sensor readings, electrical sensor readings, engine power readings, etc.
  • Examples of operational conditions of the machine may include whether the locomotive is operating in a motoring or in a dynamic braking mode of operation, whether any given subsystem in the locomotive is undergoing a self-test, whether the locomotive is stationary, whether the engine is operating under maximum load conditions, etc.
  • the repair data storage unit, the fault log data storage unit, and the operational parameter data storage unit may respectively contain repair data, fault log data and operational parameter data for a plurality of different locomotives.
  • the operational parameter data may be made up of snapshot observations, i.e., substantially instantaneous readings or discrete samples of the respective values of the operational parameters from the locomotive.
  • the snapshot observations are temporally aligned relative to the time when respective faults are generated or logged in the locomotive.
  • the temporal alignment allows for determining the respective values of the operational parameters from the locomotive prior, during or after the logging of respective faults in the locomotive.
  • the operational parameter data need not be limited to snapshot observations since substantially continuous observations over a predetermined period of time before or after a fault is logged can be similarly obtained. This feature may be particularly desirable if the system is configured for detection of trends that may be indicative of incipient failures in the locomotive.
  • FIG. 4 shows an exemplary data file 50 that combines fault log data and operational parameter data 52, such as locomotive speed, engine water temperature, engine oil temperature, call status, etc.
  • FIG. 4 further illustrates exemplary candidate snapshot anomalies and noise reduction filters that may be conveniently used to enhance the predictive accuracy of the algorithms of the present invention.
  • noise reduction filter is detection of a predetermined code, e.g., the letter S adjacent to bracket 54 in the column designated "call". In this case, this noise reduction filter allows for ignoring any faults that may have been logged while a self-test is being conducted.
  • Another example of a noise reduction filter may be based on ignoring suspect or unreliable information, such as may occur upon detection of two or more consecutive faults within a predetermined period of time. For example, as illustrated in bracketed row 56, this noise reduction rule allows to ignore the snapshot observations of operational parameters 52 whenever any fault is logged within a predetermined period of time, e.g., t, ms, as any previously logged fault.
  • bracketed row 58 another example of a noise reduction filter would allow for ignoring snapshot observations if any fault is logged within a predetermined period of time, e.g., t 2 ms, as any previously two logged faults.
  • candidate snapshot anomalies refer to one or more conditions that may be triggered based upon deviations in the snapshot data and/or the fault log data.
  • a candidate snapshot anomaly that conveniently uses predetermined operational parameter data is illustrated in the data field entry adjacent to bracket 60. In this case, the candidate snapshot anomaly would be triggered if the engine water temperature exceeds the engine oil temperature by a predetermined temperature, e.g., T,° C, and if the water temperature is above another predetermined temperature e.g., T 2 ° C.
  • this exemplary candidate snapshot anomaly Upon such conditions being met by the respective snapshot parameters, then this exemplary candidate snapshot anomaly would be triggered and would allow for declaring a cooling subsystem malfunction with a higher level of confidence than would otherwise be feasible if one were to rely on fault log data alone. It will be appreciated that using the foregoing candidate snapshot anomaly in combination with detection of one or more faults regarding the cooling subsystem will increase the probability that in fact there is a malfunction of the cooling subsystem as compared to detection of cooling subsystem faults by themselves. Another example of a candidate snapshot anomaly is illustrated by the data field entry adjacent to bracket 62.
  • the candidate snapshot anomaly would be triggered when the oil engine temperature exceeds the water engine temperature by a predetermined temperature, e.g., T,° C, and if the oil temperature is above another predetermined temperature e.g., T 2 ° C.
  • a predetermined temperature e.g., T,° C
  • T 2 ° C another predetermined temperature
  • this other exemplary candidate snapshot anomaly would allow for declaring a malfunction in the lubrication subsystem of the engine with a higher level of confidence than would otherwise be possible.
  • FIG. 5 illustrates a flow chart of one exemplary embodiment of the present invention.
  • step 72 allows for receiving the fault log data.
  • step 74 allows for executing a set of noise reduction filters upon the received fault log data to generate noise-reduced data.
  • step 76 allows for executing a set of candidate snapshot anomalies upon the noise- reduced data to generate data predictive of malfunctions of the machine.
  • Each predicted malfunction may be correlated with the repair data using statistical correlation techniques well-understood by those skilled in the art.
  • the repair data may include respective repair codes 64 and may further indicate one or more corrective actions to be taken once a specific malfunction is detected.
  • the indication may be for the operator to disengage a respective handbrake unintentionally activated, or suggest the replacement of a given replaceable unit, or in more complex situations may suggest to the operator to bring the locomotive to a selected repair site where needed specialized tools may be available to perform the repair.
  • a respective repair weight should be retrieved from a directed weight data storage unit 26 (FIG. 1) to verify that the predicted malfunction and selected repair meet the respective weight assigned to the predicted malfunction or repair.
  • the initial values for the directed weight data may be obtained based on the knowledge of experts and/or empirical data, that is, the values of the directed weight data may be initially assigned.
  • the system may be configured to automatically adjust or adapt the respective values of the directed weight data based on the cumulative knowledge acquired from such additional cases.
  • both the noise-reduction filters and the candidate snapshot anomalies may be adapted or modified based on the cumulative knowledge extracted from the additional cases.
  • FIG. 6 illustrates an exemplary embodiment wherein a candidate snapshot anomaly processor module 208, which may be part of processor 12, receives fault log data 100 and operational parameter data 52 that may be optionally filtered or noise-reduced by respective noise reduction filters 202 and 204 using the noise reduction filters discussed in the context of FIG. 4.
  • processor module 206 receives noise- reduced data both for the fault log data and the operational parameter data.
  • processor module 206 is not restricted to operating on noise- reduced data since the candidate snapshot anomalies could also be directly executed on unfiltered or raw fault log data and/or operational parameter data.
  • FIG. 7 illustrates a flow chart illustrating additional processing steps performed by processor module 206 regarding executing step 76 (FIG 5).
  • step 208 allows for combining candidate snapshot anomalies triggered by the fault log data with candidate snapshot anomalies triggered by the operational parameter data to generate data predictive of malfunctions of the machine.
  • step 210 allows for selecting at least one repair for each predicted malfunction using a plurality of weighted repairs and, as suggested above, respective combinations of distinct clusters of faults and/or observations of operational parameters.
  • FIG. 8 is a flowchart of an exemplary process 150 of the present invention for selecting or extracting repair data from repair data storage unit 20, fault log data from fault log data storage unit 22, and operational parameter data from operational parameter data storage unit 29 and generating a plurality of diagnostic cases, which are stored in a case storage unit 24.
  • case comprises a repair and one or more distinct faults or fault codes in combination with respective observations of one or more operational parameters.
  • process 150 comprises, at 152, selecting or extracting a repair from repair data storage unit 20 (FIG. 1).
  • the present invention searches fault log data storage unit 22 (FIG. 1) to select or extract, at 154, distinct faults occurring over a predetermined period of time prior to the repair.
  • operational parameter data storage unit 29 (FIG. 1) may be searched to select or extract, at 155, respective observations of the operational parameter data occurring over a predetermined period of time prior to the repair.
  • the observations may include snapshot observations, or may include substantially continuous observations that would allow for detecting trends that may develop over time in the operational parameter data and that may be indicative of malfunctions in the machine.
  • the predetermined period of time may extend from a predetermined date prior to the repair to the date of the repair. Desirably, the period of time extends from prior to the repair, e.g., 14 days, to the date of the repair. It will be appreciated that other suitable time periods may be chosen. The same period of time may be chosen for generating all of the cases.
  • the number of times each distinct fault occurred during the predetermined period of time is determined.
  • the respective values of the observations of the operational parameters is determined.
  • a plurality of repairs, one or more distinct fault cluster and respective observations of the operational parameters are generated and stored as a case, at 160.
  • a plurality of repair, respective fault cluster combinations, and respective combinations of clusters of observations of the operational parameters is generated at 162.
  • a process 250 of the present invention provides for updating directed weight data storage unit 26 to include one or more new cases. For example, once a new case is generated, a new repair, fault log data, and operational parameter data from a malfunctioning locomotive is received at 252. At 254, a plurality of distinct fault cluster combinations and clusters of observations of the operational parameters is generated.
  • the number of times each fault cluster occurred for related repairs is updated at 256 and the number of times each fault cluster occurred for all repairs are updated at 258.
  • respective values of the clusters of observations of the operational parameters that triggered respective candidate snapshot anomalies for related repairs may be averaged and updated at 260 and respective values of the operational parameters that triggered respective candidate snapshot anomalies for all repairs may be averaged and updated at 262.
  • the weighted repair, the distinct fault cluster combinations and the respective weight values for the candidate snapshot anomalies are redetermined at 264.
  • the candidate snapshot anomaly may have initially postulated that if the engine water temperature exceeds the engine oil temperature by T, 0 C, and if the water temperature is above T 2 ° C, then the candidate snapshot anomaly would declare a cooling subsystem malfunction.
  • the learning algorithm would conveniently allow for redetermining the respective temperature values required to trigger the candidate snapshot anomaly, in view of the accumulated knowledge gained from each new case.
  • candidate snapshot anomalies themselves could be modified to add observations of new parameters or delete observations from parameters that were initially believed to be statistically meaningful but in view of the cumulative knowledge acquired with each new case are proven to be of little value for triggering a respective candidate snapshot anomaly, i.e., equivalent to a "Don't Care" variable in Boolean logic.
  • further analysis of the repair data could indicate that ambient temperature may be another parameter that could aid the candidate snapshot anomaly to trigger more accurately the prediction of malfunctions of the cooling subsystem.
  • the system provides prediction of malfunctions and repair selection from hybrid analysis of fault log data and operational parameter data from a malfunctioning machine. Desirably, after verification of the repair(s) for correcting a malfunction the new case can be inputted and updated into the system.
  • repair, respective fault cluster combinations and observations of operational parameters may be generated and stored in memory when generating the weights therefor, or alternatively, be stored in either the case data storage unit, directed weight storage unit, or a separate data storage unit.
  • the present invention provides in one aspect a method and system for automatically harvesting potentially valid diagnostic cases by interleaving repair, fault log data which is not restricted to sequential occurrences of faults or error log entries and operational parameter data that could be made up of snapshot observations and/or substantially continuous observations.
  • standard diagnostic fault clusters and suitable candidate snapshot anomalies using operational parameters and/or fault data can be generated in advance so they can be identified across all cases and their relative occurrence tracked.
  • the present invention further allows use of noise reduction filters that may enhance the predictive accuracy of the diagnostic algorithms used therein.
  • the invention allows for readjusting the assigned weights to the repairs, the candidate snapshot anomalies and the noise reduction filters based on extracting knowledge that is accumulated as each new case is closed.
  • a field engineer may review each of the plurality of cases to determine whether the collected data, either fault log data and/or operational parameter data, provide a good indication of the repair. If not, one or more cases can be excluded or removed from case data storage unit 24. This review by a field engineer would increase the initial accuracy of the system in assigning weights to the repair, candidate snapshot malfunctions and fault cluster combinations.
  • FIG. 10 shows a flow chart of an exemplary embodiment of a process 350 for analyzing fault log data so as to avoid missing detection or identification of fault log data which is statistically and probabilistically relevant to early and accurate prediction of machine malfunctions.
  • step 354 allows for downloading new fault log data from the machine.
  • Step 356 allows for verifying predetermined identification parameters of the newly downloaded fault log data so as to avoid unintentionally attributing faults to the wrong locomotive.
  • Exemplary identification parameters may include road number, time of download, time fault was logged, etc.
  • this step may allow for verifying that the road number in a previously downloaded fault log actually matches the road number of the locomotive fault log presently intended to be downloaded and may further allow for verifying that the date and time in the fault log matches the present date and time.
  • Step 358 allows for retrieving prior fault log data of the machine.
  • the prior fault log may be obtained during an earlier download, such as the last download executed prior the download of step 354.
  • step 360 allows for comparing the new fault log data against the prior fault log data.
  • step 362 allows for adjusting any repair recommendations for the earlier download of fault log data based upon the comparison of the new fault log data and the prior fault log data.
  • FIG. 11 is a flowchart that illustrates further details regarding process 350 (FIG. 10).
  • step 372 allows for determining whether any new faults have occurred since the last download. If new faults have not been logged since the last download, then step 374 allows for reviewing and updating the last repair recommendation. If new faults were logged at step 372, then step 376 allows for determining whether any of the new faults are repeats of the previously logged faults, e.g., faults that previously required a recommendation. If there are repeat faults, then, as suggested above, step 374 would allow for reviewing and updating the last repair recommendation. If there are no repeat faults, then step 380 allows for determining if the newly downloaded faults are related to any previously logged faults.
  • related faults generally affect the same machine subsystem, such as power grid faults and dynamic braking faults, both generally related to the dynamic braking subsystem of the locomotive. If the newly downloaded faults are related to previously logged faults, then once again, step 374 would allow for reviewing and updating the last repair recommendation. Step 382 allows for determining whether there are any active faults. If there are active faults, then step 384 allows for assigning a respective repair action. For example, the repair assignment may require to determine if the locomotive engineer should reset the faults, or if the locomotive should be checked first by one or more repair specialists. By way of example, any open or non-reset faults will show 0.00 in the reset column.
  • step 386 allows for conducting expert analysis on the fault.
  • the expert analysis may be performed by teams of experts who preferably have a reasonably thorough understanding of respective subsystems of the locomotive and their interaction with other subsystems of the locomotive. For example, one team may address fault codes for the traction subsystem of the locomotive. Another team may address faults for the engine cooling subsystem, etc.
  • FIG. 12 is a flowchart of an exemplary process 450 for selecting or extracting repair data from repair data storage unit 20, fault log data from fault log data storage unit 22, and operational parameter data from operational parameter data storage unit 29 and generating a plurality of diagnostic cases, which are stored in a case storage unit 24.
  • case comprises a repair and one or more distinct faults or fault codes in combination with respective observations of one or more operational parameters.
  • process 450 comprises, at 452, selecting or extracting a repair from repair data storage unit 20 (FIG. 1). Given the identification of a repair, the present invention searches fault log data storage unit 22 (FIG. 1) to select or extract, at 454, distinct faults occurring over a predetermined period of time prior to the repair. Similarly, operational parameter data storage unit 29 (FIG. 1) may be searched to select or extract, at 455, respective observations of the operational parameter data occurring over a predetermined period of time prior to the repair. Once again, the observations may include snapshot observations, or may include substantially continuous observations that would allow for detecting trends that may develop over time in the operational parameter data and that may be indicative of malfunctions in the machine.
  • the predetermined period of time may extend from a predetermined date prior to the repair to the date of the repair. Desirably, the period of time extends from prior to the repair, e.g., 14 days, to the date of the repair. It will be appreciated that other suitable time periods may be chosen. The same period of time may be chosen for generating all of the cases.
  • the present invention provides in one of its aspects, a process and system for developing a fault log data analysis kit that enables users to analyze fault log data from a machine so as to identify faults and/or fault combinations predictive of machine malfunctions.
  • the kit may be deployed in any suitable form, electronic or otherwise, e.g., a flowchart, a checklist, a computer program product configured in any suitable computer-usable medium and having a computer-readable code therein for executing the respective process steps discussed above in the context of FIGS. 10 and 11, etc.
  • the output of the fault analysis kit and/or process of the present invention could be used for opening respective cases in the case data storage unit 24, such as during situations when system 10 may be unavailable, for example, due to maintenance and/or upgrading.
  • the tool analysis kit of the present invention may provide backup to system 10 as well as enhance any CBR analysis performed on the fault log data by system 10.

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Abstract

La présente invention concerne un procédé permettant d'analyser les données du journal d'incidents (200) et de dresser un instantané des données des paramètres de fonctionnement (25) de locomotives et d'autres grands matériels terrestres automoteurs soumis à des tests et diagnostics. Une phase de prise en compte (72) permet la prise en compte des données du journal d'incidents et notamment d'une pluralité de données d'incidents de ce matériel. Différentes phases d'exécution permettent de réaliser un filtrage de réduction de bruit (27) sur les données de journal d'incidents prises en compte et de produire des données de journal d'incidents où les bruits ont été réduits, puis d'exécuter une série d'anomalies instantanées candidates (28) sur les données à bruit réduit de façon à produire des données de prévision des incidents de fonctionnement du matériel.
PCT/US2000/008661 1999-04-02 2000-03-31 Analyse de donnees de fonctionnement pour le diagnostic des incidents de fonctionnement des locomotives et systeme a cet effet Ceased WO2000060464A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CA002368818A CA2368818C (fr) 1999-04-02 2000-03-31 Analyse de donnees de fonctionnement pour le diagnostic des incidents de fonctionnement des locomotives et systeme a cet effet
MXPA01009957A MXPA01009957A (es) 1999-04-02 2000-03-31 Un metodo y sistema para analizar los datos operacionales para diagnostico de fallas del funcionamiento de locomotoras.
AU40602/00A AU758061B2 (en) 1999-04-02 2000-03-31 A method and system for analyzing operational data for diagnostics of locomotive malfunctions

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US09/285,611 1999-04-02
US09/285,611 US6343236B1 (en) 1999-04-02 1999-04-02 Method and system for analyzing fault log data for diagnostics
US09/285,612 US6415395B1 (en) 1999-04-02 1999-04-02 Method and system for processing repair data and fault log data to facilitate diagnostics
US09/285,612 1999-04-02
US09/438,271 1999-11-12
US09/438,271 US6336065B1 (en) 1999-10-28 1999-11-12 Method and system for analyzing fault and snapshot operational parameter data for diagnostics of machine malfunctions
US09/447,064 1999-11-22
US09/447,064 US6622264B1 (en) 1999-10-28 1999-11-22 Process and system for analyzing fault log data from a machine so as to identify faults predictive of machine failures

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CN114118245A (zh) * 2021-11-19 2022-03-01 国网湖北省电力有限公司经济技术研究院 一种电网设备资产台账异常数据自动修复系统及方法

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CA2368818A1 (fr) 2000-10-12
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AU758061B2 (en) 2003-03-13
MXPA01009957A (es) 2003-07-14

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