Abstract: An analysis scheduler for scheduling the automatic processing of performance data through a plurality of analysis tools is disclosed. Performance data provided to some of the tools by the analysis scheduler may be specified to be within a predetermined (but variable) look-back period. The analysis tools identify faults and anomalous conditions and also create repair recommendations, and automatically create problem cases when conditions warrant, or update existing problem cases with additional data, all under control of the analysis scheduler. The problem cases are reviewed by a human user and then forwarded to the railroad for implementation.

Abstract: The present invention discloses system and method for analyzing continuous parameter data from a malfunctioning locomotive or other large land-based, self-powered transport equipment. The method allows for receiving new continuous parameter data comprising a plurality of anomaly definitions from the malfunctioning equipment. The method further allows for selecting a plurality of distinct anomaly definitions from the new continuous parameter data. Respective generating steps allow for generating at least one distinct anomaly definition cluster from the plurality of distinct anomaly definitions and for generating a plurality of weighted repair and distinct anomaly definition cluster combinations. An identifying step allows for identifying at least one repair for the at least one distinct anomaly definition cluster using the plurality of weighted repair and distinct anomaly definition cluster combinations.

Abstract: The present invention discloses a system and computer-implemented method for analyzing a fault log from a malfunctioning machine. The method includes receiving, from the malfunctioning machine, at least one of at least one fault code and at least one data code, at least one fault code and at least one anomaly code, and at least one data code and at least one anomaly code, and determining at least one of a cause of and a repair for the malfunctioning machine based on the at least one of the at least one fault code and the at least one data code, the at least one fault code and the at least one anomaly code, and the at least one data code and the at least one anomaly code. The method desirably includes receiving a fault log comprising at least one fault code and at least one data pack, parsing the at least one data pack, and generating at least one data pack code for a portion of the at least one parsed data pack.

Abstract: An algorithm for improving the probability of identifying the repair that will correct a fault aboard a machine, such as a locomotive. The invention utilizes historical fault log and repair data and further calculates the number of times a particular fault occurs in a given number of days and also the number of times a particular fault occurs on each day. Averages are calculated for these results and when the number of fault occurrences exceed some or more of those averages, then these faults are deemed statistically significant for subsequent processing.

Abstract: A method and apparatus for determining the root cause of no trouble found events in a machine is disclosed. The actual faults occurring during a predetermined time interval prior to the no trouble found event are analyzed and correlated with the no trouble found events in an effort to identify those actual faults that have a high correlation with each no trouble found event. If a high correlation is not found, then the no trouble found event is analyzed off-line to determine the root cause.

Abstract: A system (830) and method (800) for diagnosing a malfunctioning machine. A fault event is selected (806) together with sequential operating parameter data (808) from a selectively focused time interval about the fault event for evaluation of a machine (810). The selectively focused time interval may include data occurring just before, just after, or spanning the fault event. Characterizing information such as slope, rate of change, and absolute sign of the data may be derived (809) from the operating parameter data over the selectively focused time interval and used in the diagnosis. The fault event and operating parameter data may be compared to existing cases in a case database (834). A set of rules (817) or candidate anomalies (841) may be executed over the operating parameter data to further improve the accuracy of the diagnosis.

Abstract: The present invention discloses a system and method for processing repair data and fault log data from one or more machines to facilitate analysis of a malfunctioning machine. The system includes a repair data storage unit comprising a plurality of repairs and a fault log data storage unit comprising a plurality of faults. A processor generates a plurality of cases from the repair data and the fault log data. Each case comprises a repair and a plurality of distinct faults. The processor further generates, for each of the plurality of cases, at least one repair and distinct fault cluster combination, and 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.

Abstract: The present invention discloses a system and method for analyzing fault log data from a malfunctioning machine. The system comprises a processor for receiving new fault log data comprising a plurality of faults from the malfunctioning machine and selecting a plurality of distinct faults from the new fault log data. The processor generates at least one distinct fault cluster from the selected plurality of distinct faults. Thereafter, the processor predicts at least one repair for at least one distinct fault cluster using a plurality of predetermined weighted repair and distinct fault cluster combinations.

Abstract: A method for analyzing fault log data and snapshot operational parameter data from a machine undergoing diagnostics is provided. A receiving step allows for receiving fault log data comprising a plurality of faults from the machine. Respective executing steps allow for executing a set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data, and for executing a set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the machine.