Deep PCA Based Real-Time Incipient Fault Detection and Diagnosis Methodology for Electrical Drive in High-Speed Trains

Abstract

Incipient fault detection and diagnosis (FDD) is a key technology for enhancing safety and reliability of high-speed trains. This paper develops a real-time incipient FDD method named deep principal component analysis (DPCA) for electrical drive in high-speed trains. This method can effectively detect incipient faults in electrical drive before they develop into faults or failures. This scheme adopting multivariate statistics is composed of multiple data processing layers to extract more accurate signal features of electrical drive, which exhibits several salient advantages: 1) It can establish precise data models containing both systematic and noise information of electrical drive, which are helpful for incipient fault detection; 2) the incipient faults are described by multicharacteristics which can improve the fault diagnosis ability; 3) it can be easily implemented even if the system models and parameters of electrical drive are unknown. The effectiveness and feasibility of the proposed FDD scheme are authenticated via a mathematical analysis and validated via two experiments. Results of two experiments show that the missing alarm rate and detection delay by using the proposed DPCA-based FDD method are less than 10% and 0.68 s, respectively. In comparison with the standard PCA-based FDD method, the proposed DPCA-based FDD method can show its superiorities by the detailed performance comparisons.