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作 者:Yongkui Sun Yuan Cao Peng Li Shuai Su
机构地区:[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
出 处:《Chinese Journal of Electronics》2025年第1期204-211,共8页电子学报(英文版)
基 金:funded by the National Natural Science Foundation of China(Grant Nos.52202392,U1934219,52022010,52372308,and 62271486)。
摘 要: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,multiscale 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%.
关 键 词:Fault diagnosis Railway point machines Variational mode decomposition RELIEFF
分 类 号:U284.72[交通运输工程—交通信息工程及控制] U216.3[交通运输工程—道路与铁道工程]
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