Just-In-Time-Learning Multi-Block Dynamic Independent Component Analysis for Electrical Drive Systems of High-Speed Trains

Abstract

The electric drive system provides traction power for the entire high-speed train system, and its fault detection and diagnosis (FDD) has been widely studied. In this paper, a new method called just-in-time-learning multi-block dynamic independent comparative analysis (JITL-MBDICA) is proposed. The significant advantages of the FDD method based on JITL-MBDICA are: 1) It improves the matching ability of offline models with online data; 2) lt accurately detects faults through multiple modules; 3) It uses Support Vector Data Description (SVDD) to comprehensively analyze the detection results. The false alarms are reduced, The fault detection rate (FDR) is improved; 4) It is suitable for a non-Gaussian electric drive system. the effectiveness of JITL-MBDICA is verified on the high-speed train electric drive system.