A self‐adaptive phase‐segmentation and health assessment framework for point machines-Reference-Cited by-同舟云学术

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A self‐adaptive phase‐segmentation and health assessment framework for point machines

Published:2022-10-18 Issue:4 Volume:17 Page:730-743
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET Intelligent Trans Sys

Author:

Wang Ning¹²³^ORCID,Kou Linlin⁴,Zhang Huiyue¹²³,Jia Limin¹²³,Qin Yong¹²³,Wang Hongguang⁵,Wang Zhipeng¹²³

Affiliation:

1. State Key Laboratory of Rail Traffic Control & Safety No. 3 Shangyuancun, Haidian District Beijing China

2. Beijing Research Center of Urban Traffic Information Sensing and Service Technologies No.3 Shangyuancun, Haidian District Beijing China

3. School of Traffic and Transportation Beijing Jiaotong University No .3 Shangyuancun, Haidian District Beijing China

4. Beijing Mass Transit Railway Operation Corporation Ltd. Beijing China

5. Operation Headquarters Guangzhou Metro Group Guangdong China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12299

Reference35 articles.

1. EANN 2014: a fuzzy logic system trained by conjugate gradient methods for fault classification in a switch machine

2. A reliability centered approach to remote condition monitoring. A railway points case study

3. Monitoring structural deterioration of railway turnout systems via dynamic wheel/rail interaction

4. A fault detection method for railway point systems

5. An algorithm for improved performance of railway condition monitoring equipment: Alternating-current point machine case study

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