High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the reliability and safety are still not mature enough for keeping up with other aspects. The first objective of this paper is to present a comprehensive review on the fault detection and diagnosis (FDD) techniques for high-speed trains. The second purpose of this work is, motivated by the pros and cons of the FDD methods for high-speed trains, to provide researchers and practitioners with informative guidance. Then, the application of FDD for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years. Finally, the challenges and promising issues are speculated for the future investigation.

A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains

The task of condition monitoring and fault diagnosis of rolling element bearing is often cumbersome and labour intensive. Various techniques have been proposed for rolling bearing fault detection and diagnosis. The challenge however, is to efficiently and accurately extract features from signals acquired from these elements, particularly in the time–frequency domain. A new time–frequency technique, known as basis pursuit, was recently developed. This paper presents an application of this new basis pursuit method in the extraction of features from signals collected from faulty rolling bearings with inner race and outer race faults. Results obtained using this new technique were compared with discrete wavelet packet analysis (DWPA) and the matching pursuit technique. Basis pursuit represents features with very fine resolution and sparsity in the time–frequency domain thus rendering easier interpretation of the analysed results. The technique also improves the signal to noise ratio so that subsequent fault detection and identification can be conducted with confidence.

Fault diagnosis of rolling element bearings using basis pursuit

A spatial coupling model to study dynamic performance of pantograph-catenary with vehicle-track excitation

Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis

Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution

The Gini index (GI), GI II, and GI III are proven to be effective sparsity measures in the fields of machine condition monitoring and fault diagnosis, and they can be reformulated as the ratio of different quasi-arithmetic means (RQAM). Under this framework, generalized Gini indices (GGIs) have been developed for sparse quantification by applying nonlinear weights to GI, and another generalized form of GI, referred to here as power function-based Gini indices I (PFGI1s), has been introduced by using power function as the generator of quasi-arithmetic means. The GGIs with different weight parameters exhibit reliable sparse quantization capability for repetitive transient features, while their repetitive transient discriminability is lower than kurtosis and negentropy under noise contamination. PFGI1 achieves enhanced repetitive transient discriminability with increasing power exponent, showing the advantage of the generalization approach. In this paper, based on RQAM, a single-parameter generalization method for generating PFGI1s is introduced into GI II and GI III from the perspective of the quasi-arithmetic mean generator, which leads to the power function-based Gini indices II and III (PFGI2s and PFGI3s) constructed from GI II and GI III, respectively. Mathematical derivation proves that PFGI2s and PFGI3s satisfy at least five of six typical attributes of sparsity measures and are two new families of sparsity measures. Simulation analysis shows that, similar to PFGI1s, PFGI2s and PFGI3s can monotonically estimate the sparsity of the data sequence and can simultaneously achieve strong random transient resistibility and high repetitive transient discriminability compared with traditional sparsity measures. The experimental results of bearing run-to-failure demonstrate that PFGI1s, PFGI2s, and PFGI3s with appropriate power exponents can effectively quantify the repetitive transient features caused by bearing faults and can accurately characterize the bearing degradation status compared with the state-of-the-art sparsity measures.

Yao Cheng

Papers

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring