Data processing: measuring – calibrating – or testing – Measurement system – Performance or efficiency evaluation
Reexamination Certificate
2000-07-31
2003-11-18
Wachsman, Hal (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Performance or efficiency evaluation
C701S030000
Reexamination Certificate
active
06651034
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates to a method and apparatus for automatically analyzing parametric performance and fault related data from a machine, specifically from a railroad locomotive.
Cost efficient operation of a railroad requires minimization of line-of-road failures and locomotive down time. Failure of a major locomotive subsystem can cause serious damage, costly repairs, and significant operational delays. A locomotive break-down while in service is an especially costly event, requiring the dispatch of a replacement locomotive to pull the train consist and possibly rendering a track segment out of service until the train is moved. As a result, the health of the locomotive engine and its constituent subsystems is of significant concern.
Heretofore, there has been no automatic or systematic mechanism for the diagnosis of locomotive faults or for the identification of incipient problems. Instead, conventionally, the railroad has relied on regular inspections and the observation of performance anomalies by the locomotive operator. Some cursory inspection processes are accomplished while the locomotive is in service; more thorough inspections require the locomotive to be taken out of service for several days. In any case, locomotive down time, whether for inspection or repair, represents a significant railroad cost. The avoidance of these costs by accurate fault diagnosis and prediction of potential failures represents an important cost saving opportunity for the railroads.
The prior art solutions to these problems focus on the engineering design process with an objective of increasing the mean time between failure for locomotive subsystems and components. While this is certainly a commendable objective, it remains for the railroads to continue their cost containment goals through the collection and monitoring of real time performance data and fault related information directly from the locomotive, and the implementation of repairs before the problem requires significant down time.
SUMMARY OF THE INVENTION
The above mentioned difficulties and disadvantages associated with locomotive failures can be ameliorated by the present invention, which relates to a novel and non-obvious apparatus and method for analyzing real time performance and fault-related data downloaded from a fleet of locomotives.
U.S. Patent Application entitled “On-Board Monitor for a Railroad Locomotive” (filed on Oct. 25, 2000 with application Ser. No. 09/696,368, claiming the benefit of U.S. Provisional Application 60/161,965 filed on Oct. 28, 1999), owned by the Assignee of the present invention discloses and claims a method and apparatus for collecting parametric performance and fault data from an operating locomotive and transferring the data to a monitoring and diagnostic service center. The present invention describes and claims a method and apparatus for analyzing the received data to identify anomalous conditions and the source of potential or actual faults, and for recommending repair actions.
In one application of the present invention, each locomotive in a railroad's fleet of locomotives includes an on-board monitor, as described in the related application identified above. After data collection, the on-board monitor transmits performance and fault data on a regular basis to a monitoring and diagnostic service center, where the present invention analyzes the received data. There could be as many as 3,000 locomotives in a fleet, each reporting data on a daily basis. Such an enormous amount of data will easily overload a human operator. It is thus necessary to automate the execution of analysis tools so that the analysis of fault and parametric performance data from the automated data downloads can be accomplished in an efficient and productive manner.
In accordance with the teachings of the present invention, the scheduling and execution of each analysis tool occurs without human intervention and is based upon dynamic and time-critical criteria applied to the received data. For instance, one such criterion could be the priority of the queued data awaiting analysis. The present invention automatically schedules, prioritizes, and oversees the execution of one or more analysis and diagnostic tools for analyzing the locomotive data. The analysis scheduler of the present invention also conducts on-line monitoring of the downloaded data, prioritizes the data queued for each analysis tool, and ensures that all prerequisites are met before triggering execution of an analysis or diagnostic tool. In one embodiment, for example, there may be limits on the number of instances of each tool that can be executed simultaneously, and the limits may be dependent on the priority of the data. For instance, one limit applies to normal priority data and a second limit applies to high priority data. The analysis scheduler maintains and enforces these limits. In the event that a system outage occurs, the analysis scheduler automatically restarts each analysis tool that was processing when the outage occurred. In the event of an analysis tool failure, the automatic scheduler retries operation of the failed tool, until a predetermined retry limit is reached.
Following execution of the analysis tools, the present invention creates a problem case for each problem or anomaly identified by the automated analysis process. To focus the limited human resources on actual problem solving, it is desirable to automatically create the problem cases. The problem case incorporates the outputs from the multiple analysis and diagnostic tools and includes all relevant data, including symptoms, the nature of any fault, related performance parameters, diagnostic information, and repair recommendations as generated by the automated analysis process. The problem case generator displays all this information visually for viewing by a human diagnosis and repair expert.
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Bliley Richard G.
Gibson David Richard
Hedlund Eric H.
Loncher Steven
Lovelace, II John Howard
Baran Mary Catherine
Beuss Brownlee Bowdoin & Wolter, P.A.
DeAngelis Jr. John L.
Rowold Carl A.
Wachsman Hal
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