Real-time incipient fault detection for electrical traction systems of CRH2

Electrical traction systems in a high-speed train are the core parts to provide traction force for the whole train. Due to performance degradation of electronic components and the prolonged operation under variously complicated operating environments, incipient faults will inevitably happen and will evolve into faults or failures if they are not successfully detected. Currently, the univariate control charts are used to monitor electrical traction systems of high-speed trains. However, this primitive solution is unable to deal with incipient faults with satisfactory performance. In this paper, a Kullback-Leibler divergence (KLD) and independent component analysis (ICA)-based method is proposed to perform incipient fault detection (FD) in electrical traction systems. Compared with the existing ICA-based methods, the proposed strategy is more sensitive to incipient faults; meanwhile it has low computational load because estimating the probability density functions (PDFs) of the derived independent components and the residuals is avoided. On the experimental platform of the traction system for China Railway High-speed 2-type (CRH2) trains, three typical incipient faults are successfully injected, and the proposed method is successful in detecting these incipient faults.