Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Vehicle diagnosis or maintenance indication
Reexamination Certificate
1999-10-28
2002-06-11
Cuchlinski, Jr., William A. (Department: 2785)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Vehicle diagnosis or maintenance indication
C701S019000, C701S035000, C702S183000, C706S913000
Reexamination Certificate
active
06405108
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to machine systems, such as locomotives and other systems, having a plurality of subsystems, and, more particularly, to a system and process for developing diagnostics algorithms for predicting impending failures of the subsystems in the locomotive.
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, such as 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 in the various subsystems 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. 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 general need to be able to quickly and efficiently prognosticate any failures likely to occur in any of the subsystems of the machine, while minimizing the need for human interaction and optimizing the repair and maintenance needs of the subsystem so as to be 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 process for systematically developing algorithms for predicting failures in a system, such as a locomotive, having a plurality of subsystems. The process allows for conducting a failure mode analysis for a respective subsystem so as to identify target failure modes of the subsystem and/or collecting expert data relative to the respective subsystem. The process further allows for identifying, based on the identified failure modes and/or the collected expert data, one or more signals to be monitored for measuring performance of the respective subsystem. A generating step allows for generating, based on the monitored signals, a predicting signal indicative of the presence of any target failure modes in the respective subsystem.
The present invention further fulfills the foregoing needs by providing a system for developing algorithms for predicting failures in a machine having a plurality of subsystems. The system includes a module, e.g., a programmable module, configured to conduct a failure mode analysis for a respective subsystem so as to identify target failure modes of the subsystem, and/or configured to collect expert data relative to the subsystem. An identifier module is configured to identify, based on the identified target failure modes and/or collected expert data, one or more signals to be monitored for measuring performance of the respective subsystem, and a detection module is coupled to receive the monitored signals for generating a predicting signal indicative of the presence of any target failure modes in the respective subsystem.
REFERENCES:
patent: 4270174 (1981-05-01), Karlin et al.
patent: 4463418 (1984-07-01), O'Quin, II et al.
patent: 4517468 (1985-05-01), Kemper et al.
patent: 4695946 (1987-09-01), Andreasen et al.
patent: 4823914 (1989-04-01), McKinney et al.
patent: 4970725 (1990-11-01), McEnroe et al.
patent: 4977390 (1990-12-01), Saylor et al.
patent: 5113489 (1992-05-01), Cihiwsky et al.
patent: 5123017 (1992-06-01), Simpkins et al.
patent: 5157610 (1992-10-01), Asano et al.
patent: 5210704 (1993-05-01), Husseiny
patent: 5274572 (1993-12-01), O'Neill et al.
patent: 5282127 (1994-01-01), Mii
patent: 5321837 (1994-06-01), Daniel et al.
patent: 5329465 (1994-07-01), Arcella et al.
patent: 5400018 (1995-03-01), Scholl et al.
patent: 5406502 (1995-04-01), Haramaty et al.
patent: 5445347 (1995-08-01), Ng
patent: 5508941 (1996-04-01), Leplingard et al.
patent: 5528516 (1996-06-01), Yemini et al.
patent: 5566091 (1996-10-01), Schricker et al.
patent: 5594663 (1997-01-01), Messaros et al.
patent: 5633628 (1997-05-01), Denny et al.
patent: 5638296 (1997-06-01), Johnson et al.
patent: 5661668 (1997-08-01), Yemini et al.
patent: 5666534 (1997-09-01), Gilbert et al.
patent: 5678002 (1997-10-01), Fawcett et al.
patent: 5737215 (1998-04-01), Schricker et al.
patent: 5742915 (1998-04-01), Stafford
patent: 5806011 (1998-09-01), Azzaro et al.
patent: 5815071 (1998-09-01), Doyle
patent: 5845272 (1998-12-01), Morjaria et al.
patent: 5950147 (1999-09-01), Sarangapani et al.
patent: 5961567 (1999-10-01), Azzaro et al.
patent: 6014598 (2000-01-01), Duyar et al.
patent: 6175934 (2001-01-01), Hershey et al.
patent: 6216066 (2001-04-01), Goebel et al.
patent: 6243628 (2001-06-01), Bliley et al.
patent: 6246950 (2001-06-01), Bessler et al.
Isermann, R.; “Process Fault Diagnosis Based On Proc
Jammu Vinay Bhaskar
Patel Sagar Arvindbhai
Beusse Brownlee Bowdoin & Wolter PA
Cuchlinski Jr. William A.
Gibson Eric M
Mora Enrique J.
Rowold Carl A.
LandOfFree
Process and system for developing predictive diagnostics... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Process and system for developing predictive diagnostics..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Process and system for developing predictive diagnostics... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2963532