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LIU Junqi, WEN Tao, XIE Guo, et al., “Multi-time-scale Variational Mode Decomposition-based Robust Fault Diagnosis of Railway Point Machines under Multiple Noises,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.234, 2023.
Citation: LIU Junqi, WEN Tao, XIE Guo, et al., “Multi-time-scale Variational Mode Decomposition-based Robust Fault Diagnosis of Railway Point Machines under Multiple Noises,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.234, 2023.

Multi-time-scale Variational Mode Decomposition-based Robust Fault Diagnosis of Railway Point Machines under Multiple Noises

doi: 10.23919/cje.2022.00.234
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  • Author Bio:

    Junqi LIU received the B.S. and M.S. degree in electronic information engineering from Donghua University, Shanghai, China. He is currently pursuing the Ph.D. degree in control science and engineering with Xi’an University of Technology, Xi'an, China. His research interests include intelligent information processing and fault diagnosis in the railway signaling systems. (Email: 1210313022@stu.xaut.edu.cn)

    Tao WEN received his PH.D. degree from the Birmingham Centre for Railway Research and Education at the University of Birmingham, Birmingham, UK, in 2018. He is now a Professor with the Beijing Jiaotong University, Beijing, China. His research interests include federated learning, Internet of Things, train control system optimization, railway conditional monitoring, machine learning, and digital filter research. (Email: wentao@bjtu.edu.cn)

    Guo XIE received the B.S. and M.S. degrees from Xi’an University of Technology, China, in 2005 and 2008, respectively, and the D.E. degree from Nihon University, Tokyo, Japan, in 2013. He received Monbukagakusho, a Japanese Government Scholarship, from the Japanese Ministry of Education, Culture, Sports, Science and Technology. He is currently a Professor with Xi’an University of Technology. His research interests include safety and reliability of railway systems, optimal control, and stochastic control. He is a member of CAA and CCF. (Email: guoxie@xaut.edu.cn)

    Yuan CAO received the Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University, Beijing, China,in 2011. He is currently a Professor with the National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University. His research interest includes health management in high-speed railway systems. (Email: ycao@bjtu.edu.cn)

    Clive Roberts is Professor of Railway Systems at the University of Birmingham. Clive is Director of the Birmingham Centre for Railway Research and Education, which is the largest railway research group in Europe with just over 100 researchers. He works extensively with the railway industry and academia in Britain and overseas. He leads a broad portfolio of research aimed at improving the performance of railway systems, including a leading a strategic partnership in the area of data integration with Network Rail. His main research interests lie in the areas of railway traffic management, condition monitoring, energy simulation and system integration. (Email: c.roberts.20@bham.ac.uk)

  • Received Date: 2022-10-14
  • Accepted Date: 2023-06-16
  • Available Online: 2023-06-30
  • Fault diagnostics of railway point machines (RPMs) have attracted engineers’ and researchers’ attention. However, seldom have studies considered diverse noises along the track. To fulfill this aspect, a multi-time-scale variational mode decomposition (MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises. MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees. In this manner, the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale. In the experimental validation, a random position, random type, random number, and random length (4R) noise-adding algorithm helps to verify the robustness of the approach. The adequate experimental results demonstrate the superiority of the proposed MTSVMD-based fault diagnosis.
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