Data-driven fault diagnosis for dynamic traction systems in high-speed trains

Abstract

Traction systems are an important aspect of high-speed trains, and their reliable operation is crucial. With data available from trains, this paper proposes an optimal fault detection and diagnosis (FDD) strategy for dynamic traction systems. Based on the established dynamic model, using sensor measurements, a correlation-aided subspace identification technique is proposed to formulate residual signals and corresponding test statistics for fault detection. Then, a modified support vector machine (SVM) is designed for optimally solving the diagnosis bias caused by the difference in the apparent probabilities of multiple fault scenarios. The feasibility and effectiveness of the proposed optical FDD performance are illustrated in the CRRC experimental platforms.