Deep PCA-Based Incipient Fault Diagnosis and Diagnosability Analysis of High-Speed Railway Traction System via FNR Enhancement
Yunkai Wu, Xiangqian Liu, Yang Zhou
Abstract
In recent years, the data-driven based FDD (Fault Detection and Diagnosis) of high-speed train electric traction systems has made rapid progress, as the safe operation of traction system is closely related to the reliability and stability of high-speed trains. The internal complexity and external complexity of the environment mean that fault diagnosis of high-speed train traction system faces great challenges. In this paper, a wavelet transform-based FNR (Fault to Noise Ratio) enhancement is realised to highlight incipient fault information and a Deep PCA (Principal Component Analysis)-based diagnosability analysis framework is proposed. First, a scheme for FNR enhancement-based fault data preprocessing with selection of the intelligent decomposition levels and optimal noise threshold is proposed. Second, fault information enhancement technology based on continuous wavelet transform is proposed from the perspective of energy. Further, a Deep-PCA based incipient fault detectability and isolatability analysis are provided via geometric descriptions. Finally, experiments on the TDCS-FIB (Traction Drive Control System–Fault Injection Benchmark) platform fully demonstrate the effectiveness of the method proposed in this paper.