A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.

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Fault Diagnosis Based on 1D-CNN with Associated Auxiliary Tasks Boosted for Wheelset Bearings of High-Speed Trains

2020 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) ◽

10.1109/sdpc49476.2020.9353116 ◽

2020 ◽

Author(s):

Junjie Liu ◽

Huan Wang ◽

Zhiliang Liu ◽

Zilin Wang

Keyword(s):

Fault Diagnosis ◽

High Speed ◽

High Speed Trains

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Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

Structural Health Monitoring ◽

10.1177/14759217211036025 ◽

2021 ◽

pp. 147592172110360

Author(s):

Dongming Hou ◽

Hongyuan Qi ◽

Honglin Luo ◽

Cuiping Wang ◽

Jiangtian Yang

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

High Speed ◽

High Speed Train ◽

Signal Attenuation ◽

Transmission Path ◽

Bearing Fault Diagnosis ◽

Operation Conditions ◽

Fault Features ◽

High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

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