Real-Time Fault Diagnosis of Pulse Rectifier in Traction System Based on Structural Model

The pulse rectifier of a traction system in locomotives and electric multiple unit (EMUs) is usually vulnerable to performance degradation and faults due to uncertain factors, such as vibrations, aging, electromagnetic interferences. In order to ensure adequate redundancy and isolation measures in time to avoid fault propagation in traction systems, a real-time fault diagnosis method for sensors and IGBTs of the impulse rectifier is proposed, which lays a foundation for the redundant design of traction systems. Once a fault is detected in this paper, which can immediately act to the detected faults and take effective remedial measures to avoid the failure of the whole system. It is based on the structural analysis of the traction system whose structural model of interest will be established. Meanwhile, the structural model is evaluated and optimized according to the analytical relation model under various fault conditions, and the minimum structural overdetermined sets (MSOs) are obtained based on the optimized model, which can be used to isolate all faults. Using the MSOs, the redundancy relationship is deduced and the sequence residuals are generated. Afterward, the cumulative sum (CUSUM) algorithm is used for diagnosis decision making. The effectiveness of the proposed method is finally verified on a hardware-in-loop test platform, which can accurately simulate a traction system. It shows that the proposed method can achieve both good feasibility and high accuracy.

Automated summary

This paper proposes a real-time fault diagnosis method for the impulse rectifier of a traction system, which establishes a structural model of interest through structural analysis and optimization, achieves fault isolation using minimum structural overdetermined sets, and makes diagnosis decisions using the CUSUM algorithm. The effectiveness of the proposed method is verified through hardware testing, demonstrating good feasibility and accuracy.