Error detection/correction and fault detection/recovery – Data processing system error or fault handling – Reliability and availability
Reexamination Certificate
1999-11-22
2003-09-16
Beausoliel, Robert (Department: 2184)
Error detection/correction and fault detection/recovery
Data processing system error or fault handling
Reliability and availability
C714S037000
Reexamination Certificate
active
06622264
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to machine diagnostics, and more specifically, to a process and system for analyzing fault log data so as to ensure identification of faults and/or fault combinations predictive of machine malfunctions.
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 the machine are encountered. Typically, a field engineer will look at a fault log and determine whether a repair is necessary.
Approaches like neural networks, decision trees, etc., have been employed to learn over input data to provide prediction, classification, and function approximation capabilities in the context of diagnostics. Often, such approaches have required structured and relatively static and complete input data sets for learning, and have produced models that resist real-world interpretation.
Another approach, Case Based Reasoning (CBR), is based on the observation that experiential knowledge (memory of past experiences—or cases) is applicable to problem solving as learning rules or behaviors. CBR relies on relatively little pre-processing of raw knowledge, focusing instead on indexing, retrieval, reuse, and archival of cases. In the diagnostic context, a case refers to a problem/solution description pair that represents a diagnosis of a problem and an appropriate repair.
CBR assumes cases described by a fixed, known number of descriptive attributes. Conventional CBR systems assume a corpus of fully valid or “gold standard” cases that new incoming cases can be matched against.
U.S. Pat. No. 5,463,768 discloses an approach which uses error log data and assumes predefined cases with each case associating an input error log to a verified, unique diagnosis of a problem. In particular, a plurality of historical error logs are grouped into case sets of common malfunctions. From the group of case sets, common patterns, i.e., consecutive rows or strings of data, are labeled as a block. Blocks are used to characterize fault contribution for new error logs that are received in a diagnostic unit. Unfortunately, for a continuous fault code stream where any or all possible fault codes may occur from zero to any finite number of times and where the fault codes may occur in any order, predefining the structure of a case is nearly impossible.
U.S. patent application Ser. No. 09/285,611, assigned to the same assignee of the present invention and herein incorporated by reference, 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 Ser. No. 09/285,612, assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method for analyzing new fault log data from a malfunctioning machine in which the system and method are not restricted to sequential occurrences of fault log entries, and wherein the system and method predict one or more repair actions using predetermined weighted repair and distinct fault cluster combinations.
Further, U.S. patent application Ser. No. 09/495,696, titled, “A Method and System for Analyzing Fault and Snapshot Operational Parameter Data For Diagnostics of Machine Malfunctions”, and assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method that uses snapshot observations of operational parameters from the machine in combination with the fault log data in order to further enhance the predictive accuracy the diagnostic algorithms used therein. Thus, it will be appreciated that the fault log data may conveniently comprise snapshot data, that is, substantially instantaneous observations of predetermined operational parameters of the machine recorded during the logging of a respective fault. In each of the foregoing approaches, it would be desirable to have a process and system that allows a field or diagnostic engineer or any other personnel involved in maintaining and/or servicing the machine to systematically analyze the fault log data so as to identify respective faults and/or respective combinations of faults that otherwise could be missed. It will be appreciated that since the fault log data contains useful information in order to detect incipient failures, it is desirable to accurately identify any such faults and/or combinations so that such personnel is able to proactively make repair recommendations and thus avoid loss of good will with clients as well as costly delays that could result in the event of a mission failure of the machine. An example of a mission failure would be a failed locomotive unable to deliver cargo to its destination and possibly causing traffic gridlock in a given railtrack. It would be further desirable to be able to provide to such personnel an inexpensive and user-friendly fault analysis kit, such as a flowchart, check list, software program, etc., that would quickly allow such personnel to compare any new fault log data downloaded from the machine with prior fault log data of the same machine so as to be able to issue accurate and reliable repair recommendations to the entity responsible for operating the locomotive.
BRIEF SUMMARY OF THE INVENTION
Generally speaking, the present invention fulfills the foregoing needs by providing a process for analyzing fault log data from a machine so as to identify respective faults and/or fault combinations predictive of machine malfunctions. The process allows for downloading new fault log data from the machine. The process further allows for retrieving prior fault log data of the machine. 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, and 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. In another aspect of this invention, it will be appreciated that 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 machine so as to be able to identify the respective faults and/or fault combinations which are predictive of machine malfunctions of the machine.
The present invention further fulfills the foregoing needs by providing a system for analyzing fault log data from a machine. The system includes means for downloading new fault log data from the machine. The system further includes means for retrieving prior fault log data of the machine. The prior fault log data may be generally obtained during an earlier download relative to a present download of new fault log data. Comparing means allows for comparing the new fault log data against the prior fault log data, and adjusting means 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.
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Bliley Richard G.
Roddy Nicholas E.
Beausoliel Robert
Beusse Brownlee Bowdoin & Wolter PA
Chu Gabriel
General Electric Company
Mora, Esq. Enrique J.
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