Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals

Abstract

Abstract Condition monitoring of railway point machines is important for train operation safety and effectiveness. Referring to the fields of mechanical equipment fault detection, this paper proposes a fault detection and identification strategy of railway point machines via vibration signals. A comprehensive feature distilling approach by combining variational mode decomposition (VMD) energy entropy and time- and frequency-domain statistical features is presented, which is more effective than single type of feature. The optimal set of features was selected with ReliefF, which helps improve the diagnosis accuracy. Support vector machine (SVM), which is suitable for a small sample, is adopted to realize diagnosis. The diagnosis accuracy of the proposed method reaches 100%, and its effectiveness is verified by experiment comparisons. In this paper, vibration signals are creatively adopted for fault diagnosis of railway point machines. The presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.