Research on fault diagnosis of a railway point machine based on a multi-entropy feature extraction method and support vector machine

Abstract

Abstract A new fault diagnosis method is proposed to effectively extract the fault features of the sound signal of typical faults of ZDJ9 railway point machines. A multi-entropy feature extraction method is proposed by combing multi-scale permutation entropy and wavelet packet entropy. Firstly, empirical mode decomposition is performed on sound signals to obtain modal components with different time scales. Then, multi-scale permutation entropy is extracted from these components. Meanwhile, the wavelet packet entropy of the sound signals of these sensitive nodes is obtained by analysing the reconstructed signals of the last layer nodes. Since the multi-scale permutation entropy and the wavelet packet entropy can distinguish the subtle features of the signal, the subtle features of the original signal can be obtained as the feature vector of the ZDJ9 railway point machine in different states. To reduce the redundant information among the high-dimensional features, ReliefF is utilized. Finally, a support vector machine (SVM) is used to judge the fault type of a ZDJ9 railway point machine.