Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

被引:5
|
作者
Liu, Junqi [1 ]
Wen, Tao [2 ]
Xie, Guo [1 ]
Cao, Yuan [3 ]
机构
[1] Xian Univ Technol, Shaanxi Key Lab Complex Syst Control & Intelligent, Xian 710048, Shaanxi, Peoples R China
[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
[3] Beijing Jiaotong Univ, Natl Engn Res Ctr Rail Transportat Operat Control, Beijing 100044, Peoples R China
关键词
railway point machine (RPM); fault diagnosis; modified multi-scale symbolic dynamic entropy (MMSDE); fuzzy board learning system (BLS); TIME-SERIES ANALYSIS; APPROXIMATE ENTROPY; SYSTEM;
D O I
10.1093/tse/tdac065
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Railway point machine (RPM) condition monitoring has attracted engineers' attention for safe train operation and accident prevention. To realize the fast and accurate fault diagnosis of RPMs, this paper proposes a method based on entropy measurement and broad learning system (BLS). Firstly, the modified multi-scale symbolic dynamic entropy (MMSDE) module extracts dynamic characteristics from the collected acoustic signals as entropy features. Then, the fuzzy BLS takes the above entropy features as input to complete model training. Fuzzy BLS introduces the Takagi-Sugeno fuzzy system into BLS, which improves the model's classification performance while considering computational speed. Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.
引用
收藏
页数:7
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