The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

WANG Juhan; GAO Ying; CAO Yuan; TANG Tao

doi:10.23919/cje.2021.00.428

Volume 33 Issue 1

Jan. 2024

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Article Navigation > Chinese Journal of Electronics > 2024 > 33(1): 274-281

Juhan WANG, Ying GAO, Yuan CAO, et al., “The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 274–281, 2024 doi: 10.23919/cje.2021.00.428

Citation:

Juhan WANG, Ying GAO, Yuan CAO, et al., “The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 274–281, 2024 doi: 10.23919/cje.2021.00.428

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The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

doi: 10.23919/cje.2021.00.428

WANG Juhan^1
,,
GAO Ying²,
CAO Yuan^{3
,
,},
TANG Tao⁴

1.
School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
Graduate Department, China Academy of Railway Science, Beijing 100044, China
3.
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
4.
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China

More Information

Author Bio:
Juhan WANG was born in 1989. He received the B.E. degree in automation (Railway signal) and M.E. degree in control engineering from Beijing Jiaotong University. He is a Ph.D. candidate in traffic information engineering and control. His research interests include safety computer and SIL determination. (Email: 16111038@bjtu.edu.cn)

Ying GAO received the B.S. degree in electrical engineering and automation from Qingdao Agriculture University, Qingdao, China, in 2008, the M.S. degree in detection technology and automation from Beijing Jiaotong University, Beijing, in 2010, and the Ph.D. degree in traffic information engineering and control from the China Academy of Railway Sciences in 2019. She is currently a Safety Assessment Engineer with China Academy of Railway Sciences Corporation Ltd., and a Product Certification Engineer with Railway Signal Equipment. Her research interests include railway signal and train operation control

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. And now he is an Associate Professor of Beijing Jiaotong University. 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 communications. His research interest focuses on the possibility and suitability of new wireless communications in high-speed train systems. (Email: ycao@bjtu.edu.cn)

Tao TANG received the Ph.D. degree from the Chinese Academy of Sciences, Beijing, China, in 1991. His research interests include both high-speed and urban railway train control systems, as well as intelligent control theory. He is the Academic Pacesetter with the National Key Subject Traffic Information Engineering and Control and the Director of the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. He is also a Specialist with the National Development and Reform Commission and the Beijing Urban Traffic Construction Committee
Corresponding author: Email: ycao@bjtu.edu.cn
Received Date: 2021-12-13
Accepted Date: 2022-05-13

Available Online: 2022-11-21

Publish Date: 2024-01-05

Abstract

Abstract

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar, the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.
- Multi-classification support vector machine,
- Adaptive network-based fuzzy inference system,
- Train air braking system,
- Fault-tolerant voting,
- Multi-sensors

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References(26)

References

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