首页|Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy
Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy
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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 vari-ational 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 diagnosisRailway point machineVibration signalVariational mode decompositionTwo-stage feature selection
Yongkui SUN、Yuan CAO、Peng LI、Guo XIE、Tao WEN、Shuai SU
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National Engineering Research Center of Rail Transportation Operation and Control System,Beijing Jiaotong University,Beijing 100044,China
School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xi'an University of Technology,Shaanxi 710048,China
Frontiers Science Center for Smart High-speed Railway System,Beijing Jiaotong University,Beijing 100044,China
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Fundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of ChinaNational Science Fund for Excellent Young ScholarsNational Natural Science Foundation of China
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