Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Vehicle diagnosis or maintenance indication
Reexamination Certificate
1999-09-07
2001-06-05
Zanelli, Michael J. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Vehicle diagnosis or maintenance indication
C701S019000
Reexamination Certificate
active
06243628
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to systems (e.g., locomotives) that are made up of a plurality of subsystems, and, more particularly, to a system and method using trend patterns detected in log data of a plurality of subsystems of the locomotive for predicting impending failures in the subsystems.
As will be appreciated by those skilled in the art, a locomotive is a complex electromechanical system comprised of several complex subsystems. Each of these subsystems is built from components which over time fail. The ability to automatically predict failures before they occur in the locomotive subsystems is desirable for several reasons. For example, that ability is important for reducing the occurrence of primary failures which result in stoppage of cargo and passenger transportation. These failures can be very expensive in terms of lost revenue due to delayed cargo delivery, lost productivity of passengers, other trains delayed due to the failed one, and expensive on-site repair of the failed locomotive. Further, some of those primary failures could result in secondary failures that in turn damage other subsystems and/or components. It will be further appreciated that the ability to predict failures before they occur would allow for conducting condition-based maintenance, that is, maintenance conveniently scheduled at the most appropriate time based on statistically and probabilistically meaningful information, as opposed to maintenance performed regardless of the actual condition of the subsystems, such as would be the case if the maintenance is routinely performed independently of whether the subsystem actually needs the maintenance or not. Needless to say, a condition-based maintenance is believed to result in a more economically efficient operation and maintenance of the locomotive due to substantially large savings in cost. Further, such type of proactive and high-quality maintenance will create an immeasurable, but very real, good will generated due to increased customer satisfaction. For example, each customer is likely to experience improved transportation and maintenance operations that are even more efficiently and reliably conducted while keeping costs affordable since a condition-based maintenance of the locomotive will simultaneously result in lowering maintenance cost and improving locomotive reliability.
Previous attempts to overcome the above-mentioned issues have been generally limited to diagnostics after a problem has occurred, as opposed to prognostics, that is, predicting a failure prior to its occurrence. For example, previous attempts to diagnose problems occurring in a locomotive have been performed by experienced personnel who have in-depth individual training and experience in working with locomotives. Typically, these experienced individuals use available information that has been recorded in a log. Looking through the log, the experienced individuals use their accumulated experience and training in mapping incidents occurring in locomotive subsystems to problems that may be causing the incidents. If the incident-problem scenario is simple, then this approach works fairly well for diagnosing problems. However, if the incident-problem scenario is complex, then it is very difficult to diagnose and correct any failures associated with the incident and much less to prognosticate the problems before they occur.
Presently, some computer-based systems are being used to automatically diagnose problems in a locomotive in order to overcome some of the disadvantages associated with completely relying on experienced personnel. Once again, the emphasis on such computer-based systems is to diagnose problems upon their occurrence, as opposed to prognosticating the problems before they occur. Typically, such computer-based systems have utilized a mapping between the observed symptoms of the failures and the equipment problems using techniques such as a table look up, a symptom-problem matrix, and production rules. These techniques may work well for simplified systems having simple mappings between symptoms and problems. However, complex equipment and process diagnostics seldom have simple correspondences between the symptoms and the problems. Unfortunately, as suggested above, the usefulness of these techniques have been generally limited to diagnostics and thus even such computer-based systems have not been able to provide any effective solution to being able to predict failures before they occur.
In view of the above-mentioned considerations, there is a need to be able to quickly and efficiently prognosticate any failures before such failures occur in the locomotive subsystems, while minimizing the need for human interaction and optimizing the repair and maintenance needs of the subsystem so as to able to take corrective action before any actual failure occurs.
BRIEF SUMMARY OF THE INVENTION
Generally speaking, the present invention fulfills the foregoing needs by providing a computer-based method for predicting impending failures in a system, such as a locomotive, aircraft, power plant, etc., having a plurality of subsystems. The method allows for storing log data indicative of respective incidents or events that may occur as each of the subsystems is operative. A detecting step allows for detecting predetermined trend patterns in the log incident data. A plurality of externally-derived tables containing diagnostic knowledge data may be optionally provided. In this case, a matching step would allow for matching a detected trend pattern with one or more of the tables containing diagnostic knowledge data so as to generate a matched trend pattern. A mapping step allows for mapping each respective matched or detected trend pattern into a respective prediction of an impending failure of a respective one of the subsystems of the locomotive, and an informing or outputting step allows for informing a respective user of the failure prediction so as to allow the user to take corrective action before the predicted failure occurs.
The present invention may further fulfill the foregoing needs by providing a system for predicting impending failures in a locomotive having a plurality of subsystems. The system may comprise a storage unit, such as an electronic database, having a first subsection for storing log data indicative of respective incidents that may occur as each of the subsystems is operative. A trend detector is coupled to receive the log data from the database to detect predetermined trend patterns in the received log data. A diagnostic knowledge database may be optionally configured to store a plurality of externally-derived tables of diagnostic knowledge data. A matching module is coupled to receive a detected trend pattern from the trend detector and, may be optionally coupled to the diagnostic knowledge database to match the detected trend pattern with one or more of the tables of diagnostic knowledge so as to generate a matched trend pattern. The matching module includes a mapping module configured to map each respective matched or detected trend pattern into a respective prediction of an impending failure of a respective one of the subsystems of the locomotive. Lastly, module output means may also be provided for informing a respective user of a respective failure prediction so as to allow the user to take corrective action before the impending failure actually occurs.
REFERENCES:
patent: 5463768 (1995-10-01), Cuddihy et al.
patent: 5566091 (1996-10-01), Schricker et al.
patent: 5845272 (1998-12-01), Morjaria et al.
patent: 5950147 (1999-09-01), Sarangapani et al.
Bliley Richard Gerald
Jammu Vinay Bhaskar
Schneider William Roy
Breedlove Jill
General Electric Company
Rowold Carl
Zanelli Michael J.
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