Abstract: A method of servicing a mobile asset subject to utilization loss factors delaying the completion of servicing and the timely return of the asset to operation including identifying utilization loss factors that could delay the timely completion of servicing of the mobile asset. Identifying service procedures associated with each utilization loss factor and providing a database of events associated with the service procedures the events indicative of a status of completion of the procedures. A schedule may be established for at least one event for the timely completion of the respective service procedure. Monitoring the at least one event to determine when the event occurred relative to the scheduled time and if the at least one event is late, providing notice that the relevant utilization loss factor is likely to cause delay in the timely return of the asset to operation unless corrective action is taken.

Abstract: A system and method for improving a causal network is provided. A new apriori probability is determined for a repair or a configuration factor within the causal network and compared to an old apriori probability. If the new apriori probability differs from the old apriori probability by more than a predetermined amount, the causal network is updated. Further, in another aspect, a causal network result is stored for a causal network, wherein the causal network includes a plurality of root causes with a symptom being associated with each of said root causes. An existing link probability is related to the symptom and root cause. An expert result or an actual data result related to each of the symptoms is stored. A new link probability is computed based on the stored causal network result, and expert result or the actual data result.

Abstract: A method for analyzing fault log data and repair data to estimate time before a machine-disabling failure occurs is provided. The method allows for searching in a database of historical fault log data from a plurality of machines for the occurrence of respective fault patterns indicative of incipient failures of a respective machine subsystem. The method further allows for searching in a database of historical repair data for respective repairs executed on the respective machine subsystem. The method also allows for computing elapsed time between respective occurrences of the fault patterns and the executed repairs.

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: A system and method for improving a causal network is provided. A new apriori probability is determined for a repair or a configuration factor within the causal network and compared to an old apriori probability. If the new apriori probability differs from the old apriori probability by more than a predetermined amount, the causal network is updated. Further, in another aspect, a causal network result is stored for a causal network, wherein the causal network includes a plurality of root causes with a symptom being associated with each of said root causes. An existing link probability is related to the symptom and root cause. An expert result or an actual data result related to each of the symptoms is stored. A new link probability is computed based on the stored causal network result, and expert result or the actual data result.

Abstract: An on-board monitor for a railroad locomotive is disclosed. The on-board monitor interfaces with the controller subsystems of the locomotive to collect parametric performance data. The specific data to be collected and the collection intervals are defined at a remote service center and transmitted to the on-board monitor. The on-board monitor also includes the capability to collect additional data or collect data more frequently in response to the results of certain triggering events.

Abstract: An intelligent analysis apparatus and method for fluid filled electrical equipment includes sensors for measuring various parameters of the electrical equipment. Analytical models calculate parameters based on measurements of other parameters. The measured and calculated parameters are compared and the result of the comparison is used as an indicator in a causal network. The probabilities of the causal network are recalculated by a belief network. The analytical models are adjusted over time to account for acceptable changes in behavior of the equipment over time. The output of the causal network can be used for diagnostic and prognostic indication.

Abstract: A process and system for developing an algorithm for predicting failures in a system, such as a locomotive, having a plurality of subsystems is provided. 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 includes a step 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 the identified target failure modes in the respective subsystem.

Abstract: A computer-based method and system for predicting impending failures in a system, such as a locomotive, aircraft, power plant, etc., having a plurality of subsystems is provided. 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 mapping step allows for mapping each 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.