Vibration-based fault diagnosis for railway point machines using VMD and multiscale fluctuation-based dispersion entropy

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 variational mode decomposition（VMD） and multiscale fluctuation-based dispersion entropy（MFDE） is developed, which is verified a more effective tool for feature selection. Then, a two-stage feature selection method based on Fisher discrimination and Relief F is proposed, which is validated more powerful than signle feature selection methods. Finally, support vector machine（SVM） 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.