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Review Article | Open Access

Vibration-based bearing fault diagnosis of high-speed trains: A literature review

Wanchun HuaGe Xina( )Jiayi WuaGuoping AnbYilei LicKe Fengd,eJerome Antonif
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Center of Safety Technology, National Railway Administration, Beijing 100166, China
Bogie Technical Center, CRRC Tangshan Co., Ltd., Tangshan 064000, China
Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, United Kingdom
Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
INSA Lyon, University of Lyon, Villeurbanne 69621, France
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Abstract

Due to the advantages of comfort and safety, high-speed trains are gradually becoming the mainstream public transport in China. Since the operating speed and mileage of high-speed trains have achieved rapid growth, it is more and more urgent to ensure their reliability and safety. As an important component in the bogies of high-speed trains, the health state of the bearing directly affects the operational safety of the trains. It is therefore necessary to diagnoze the faults of bearings in the bogies of high-speed trains as early as possible. In this paper, the bearing fault diagnostic methods for high-speed trains have been systematically summarized with their challenges and perspectives. First, it briefly introduces the structure of bearings in the bogies as well as the fault characteristic frequencies. Then, a brief review of the research on vibration-based signal processing methods and machine learning methods has been provided. Finally, the challenges and future developments of vibration-based bearing fault diagnostic methods for high-speed trains have been analyzed.

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High-speed Railway
Pages 219-223

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Cite this article:
Hu W, Xin G, Wu J, et al. Vibration-based bearing fault diagnosis of high-speed trains: A literature review. High-speed Railway, 2023, 1(4): 219-223. https://doi.org/10.1016/j.hspr.2023.11.001

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Received: 28 September 2023
Revised: 01 November 2023
Accepted: 09 November 2023
Published: 22 November 2023
© 2023 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).