Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

SUN Yongkui; CAO Yuan; LI Peng; XIE Guo; WEN Tao; SU Shuai

doi:10.23919/cje.2022.00.075

Volume 33 Issue 3

May 2024

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Article Navigation > Chinese Journal of Electronics > 2024 > 33(3): 803-813

Yongkui SUN, Yuan CAO, Peng LI, et al., “Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 803–813, 2024 doi: 10.23919/cje.2022.00.075

Citation:

Yongkui SUN, Yuan CAO, Peng LI, et al., “Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 803–813, 2024 doi: 10.23919/cje.2022.00.075

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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

doi: 10.23919/cje.2022.00.075

SUN Yongkui^1
,,
CAO Yuan^{1
,
,},
LI Peng²,
XIE Guo³,
WEN Tao²,
SU Shuai⁴

1.
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
2.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
3.
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Shaanxi 710048, China
4.
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 Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University in 2016 and 2021, respectively, where he is now an Associate Professor. His research interests include fault diagnosis and condition monitoring in train control systems, and life prediction for railway key equipments. (Email: sunyk@bjtu.edu.cn)

Yuan CAO received the B.S. degree in electric engineering and automation from Dalian Jiaotong University and Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University 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 train control systems. (Email: ycao@bjtu.edu.cn)

Peng LI received the Ph.D. degree from Harbin Institute of Technology, Harbin, China, in 2009. He undertook postdoctoral research at Beijing Jiaotong University, China, from 2009 to 2011, and visiting research at University of Melbourne, Australia, from 2013 to 2014. He is with School of Electronic and Information Engineering, Beijing Jiaotong University. His research interests include automatic control, renewable energies, and transportation systems

Guo XIE received the B.S. degree and M.S. degree from Xi’an University of Technology, China, in 2005 and 2008, and received the D.E. degrees from Nihon University, Tokyo, Japan, in 2013. He was a Japanese Government Scholarship holder from Japanese Ministry of Education, Culture, Sports, Science and Technology (Monbukagakusho). He is currently a Professor at Xi’an University of Technology. His research interests include safety and reliability of railway system, optimal control and stochastic control. He is a member of IEEE, CAA and CCF

Tao WEN received the B.E. degree from the School of Computer Science, Hangzhou Dianzi University, Hangzhou, China, and master degree from the Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK, in 2011 and 2013, respectively. From 2013 to 2017, he was a Ph.D. candidate at the Birmingham Centre for Railway Research and Education at the University of Birmingham, Birmingham, UK, and received the Ph.D. degree in 2018. Now, he is working in the Beijing Jiaotong University. His research interests include CBTC system optimization, railway signalling simulation, railway condition monitoring, wireless signal processing and digital filter research

Shuai SU received the Ph.D. degree 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. 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
Corresponding author: Email: ycao@bjtu.edu.cn
Received Date: 2022-04-07
Accepted Date: 2022-07-06

Available Online: 2022-08-06

Publish Date: 2024-05-05

Abstract

Abstract

As one of the most important railway signaling equipment, railway point machines undertake the major task of ensuring train operation safety. Thus fault diagnosis for railway point machines becomes a hot topic. Considering the advantage of the anti-interference characteristics of vibration signals, this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals. A feature extraction method combining variational mode decomposition (VMD) and multiscale fluctuation-based dispersion entropy is developed, which is verified a more effective tool for feature selection. Then, a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed, which is validated more powerful than single feature selection methods. Finally, support vector machine is utilized for fault diagnosis. Experiment comparisons show that the proposed method performs best. The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively. Especially, it is a try to use new means for fault diagnosis on railway point machines, which can also provide references for similar fields.
- Fault diagnosis,
- Railway point machine,
- Vibration signal,
- Variational mode decomposition,
- Two-stage feature selection

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References(49)

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