Fault Diagnosis for Railway Point Machines Using VMD Multi-scale Permutation Entropy and ReliefF Based on Vibration Signals

SUN Yongkui; CAO Yuan; LI Peng; SU Shuai

doi:10.23919/cje.2023.00.258

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Yongkui SUN, Yuan CAO, Peng LI, et al., “Fault Diagnosis for Railway Point Machines Using VMD Multi-scale Permutation Entropy and ReliefF Based on Vibration Signals,” Chinese Journal of Electronics, vol. 34, no. 1, pp. 1–8, 2025 doi: 10.23919/cje.2023.00.258

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Yongkui SUN, Yuan CAO, Peng LI, et al., “Fault Diagnosis for Railway Point Machines Using VMD Multi-scale Permutation Entropy and ReliefF Based on Vibration Signals,” Chinese Journal of Electronics, vol. 34, no. 1, pp. 1–8, 2025 doi: 10.23919/cje.2023.00.258

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Fault Diagnosis for Railway Point Machines Using VMD Multi-scale Permutation Entropy and ReliefF Based on Vibration Signals

doi: 10.23919/cje.2023.00.258

SUN Yongkui^{1, 2
,},
CAO Yuan^{1, 2
,
,},
LI Peng^2
,,
SU Shuai^3
,

1.
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
2.
School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
3.
Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing 100044, China

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Author Bio:
Yongkui SUN received the B.S. degree in automation (railway signal) and the Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University, Beijing, China, in 2016 and 2021, respectively. Now he is an Associate Professor with National Engineering Research Center of Rail Transportation Operation Control System/the School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China. His research interests include fault diagnosis and condition monitoring in train control systems. (Email: sunyk@bjtu.edu.cn)

Yuan CAO received the B.S. degree in electric engineering and automation from Dalian Jiaotong University, Dalian, China, and the Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University, Beijing, China, in 2004 and 2011, respectively, where he is now a Professor. Since 2006, he has participated in many engineering practice, especially in the signal and communication system of high-speed railway. He has taken part in several key national research projects in the field of high-speed train control systems. His research interests include health management in high speed railway system. (Email: ycao@bjtu.edu.cn)

Peng LI received the Ph.D. degree in aircraft design from Harbin Institute of Technology, Harbin, China, in 2009. He undertook postdoctoral research at Beijing Jiaotong University, Beijing, China, during 2009 to 2011, and was a Visiting Research at University of Melbourne, Melbourne, Australia during 2013 to 2014. He is currently with the School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China. His research interests include automatic control, renewable energies, and transportation systems. (Email: lipeng@bjtu.edu.cn)

Shuai SU received the Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University, Beijing, China, in 2016. He is currently working as the Deputy Director in the Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing, China. His current research interests include energy-efficient operation and control in railway systems, intelligent train control and dispatching, such as timetable optimization, optimal driving strategy and rescheduling. (Email: shuaisu@bjtu.edu.cn)
Corresponding author: Email: ycao@bjtu.edu.cn
Received Date: 2023-07-22
Accepted Date: 2024-01-26

Available Online: 2024-06-07

Abstract

Abstract

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition (VMD) is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multi-scale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.
- Fault diagnosis,
- Railway point machines,
- Variational mode decomposition (VMD),
- ReliefF

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References(44)

References

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