Multiblock Dynamic Slow Feature Analysis-Based System Monitoring for Electrical Drives of High-Speed Trains

The electrical drive system of high-speed trains is a key subsystem to ensure the continuous supply of train power and stable operation. By the use of local information, this article presents a method called multiblock dynamic slow feature analysis (MBDSFA) with its application in the electrical drive system of high-speed trains. First, the relevance among all variables of electrical drive systems is calculated by using mutual information, based on which all variables are divided into blocks. Then, the dynamic slow feature analysis-based system monitoring scheme is employed for each subblock, and the local characteristics of electrical drive systems are analyzed via two kinds of test statistics. All subblocks are integrated based on the Bayesian inference to obtain the global monitoring results. Finally, the effectiveness and feasibility of the proposed approach are verified through the case study on the electrical drive system of high-speed trains.