Reliability assessment and lifetime prediction for train traction system considering multiple dependent components

The traction system, serving as the power core of urban rail transit trains, plays a crucial role in ensuring the safe operation of the trains. Reliability assessment and lifespan prediction are investigated in order to tackle the difficulties brought about by the traction system’s intricate structure and numerous failure types. The physics of failure model for motor demagnetization and insulated gate bipolar transistor (IGBT) are constructed. The degradation processes for those performance indicators are described by the Wiener process fusing failure mechanism while considering unit-to-unit variability. The Copula function is used to describe the dependent relationship between performance indicators. As for off-line parameter estimation, the Bayesian Markov chain Monte Carlo method estimates unknown parameters. As for online remaining useful life prediction, the algorithm combining Bayesian and expectation-maximization is implemented to update unknown parameters. The proposed model and algorithm are validated by the degradation data of the traction system. The results indicate that the reliability model considering the dependent relationship between the motor and IGBT improves the accuracy of reliability assessment. The remaining useful life prediction accuracy is improved by the parameter updating approach that combines Bayesian and expectation-maximization.