Error detection/correction and fault detection/recovery – Data processing system error or fault handling – Reliability and availability
Reexamination Certificate
1999-04-02
2002-07-02
Beausoleil, Robert (Department: 2184)
Error detection/correction and fault detection/recovery
Data processing system error or fault handling
Reliability and availability
C714S025000, C714S026000
Reexamination Certificate
active
06415395
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to machine diagnostics, and more specifically, to a system and method for processing historical repair data and fault log data to facilitate analysis of a malfunctioning machine.
A machine such as locomotive includes elaborate controls and sensors that generate faults when anomalous operating conditions of the locomotive 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.
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.
Therefore, there is a need for 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.
SUMMARY OF THE INVENTION
The above-mentioned needs are met by the present invention which provides in one embodiment a system for processing repair data comprising a plurality of repairs and fault log data comprising a plurality of faults from one or more machines to facilitate analysis of a malfunctioning machine. The system includes means for generating a plurality of cases from the repair data and the fault log data. Each case comprises a repair and a plurality of distinct faults. Generating means generates, for each of the plurality of cases, at least one repair and distinct fault cluster combination, and assigning means assigns, to each of the repair and distinct fault cluster combinations, a weight, whereby weighted repair and distinct fault cluster combinations facilitate prediction of at least one repair for the malfunctioning machine.
The assigning means for assigning a weight, for each repair and distinct fault cluster combination, may comprise dividing means for dividing the number of times the combination occurs in cases comprising related repairs by the number of times the combination occurs in the plurality of cases.
The system is also readily updated by including generating means for generating a new case from repair data and fault log data in which the new case comprises a repair and a plurality of distinct faults. Generating means generates, for the new case, a plurality of fault clusters for the plurality of distinct faults, and redetermining means redetermine a weight for each of the plurality of repair and distinct fault cluster combinations to include the new case.
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Roddy Nicholas Edward
Varma Anil
Beausoleil Robert
Breedlove Jil M.
Chu Gabriel
Goldman David C.
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