PRECEDENT-FREE FAULT LOCALIZATION AND DIAGNOSIS FOR HIGH SPEED TRAIN DRIVE SYSTEMS

In this paper, a framework for localization of sources of unprecedented faults in the drive train system of high speed trains is presented. The framework utilizes distributed anomaly detection, with anomaly detectors based on the recently introduced Growing Structure Multiple Model Systems (GSMMS) models. Physics based models of the drive system and its pertinent subsystems were derived and were calibrated using data collected over several actual trips on a high speed train. Simulation results demonstrate the ability to localize faults within various parts of the drive train system without the need for models of the underlying faults. In addition, traditional model based diagnosis was utilized for positive identification of faults, with signals emitted by the systems in the presence of those faults being available for modeling and subsequent recognition of faulty behavior