A Bayesian Network Approach for Imbalanced Fault Detection in High Speed Rail Systems

Safety and reliability of High Speed Trains (HSTs) are crucial factors for their development as mass transport means. For this reason, they are highly monitored systems, and large amounts of data are collected and used for efficient operation and maintenance. In this paper, we focus on extracting knowledge from these data for fault detection in the braking system of HSTs. A probabilistic, explainable framework is proposed, based on an objective-oriented Bayesian Network (BN). A symmetric uncertainty-based feature selection method is combined with BN, for the first time, for reducing the dimensionality of the original data. The imbalance ratio of the data can be up to more than 300 and sensitivity analysis of the method is performed. Experiment results show that the proposed approach is more accurate than published method.

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