LAUSR

Railway point machines (RPMs) is one of the most vital devices closely related to the efficiency and safety of train operation. Considering the advantages of contactlessness and easy-to-collect of sound signals, a novel sound-based fault diagnosis method for RPMs is proposed. First, fractional calculus is introduced to wavelet packet decomposition energy entropy (WPDE). Fractional WPDE (FWPDE) is then proposed, which is verified to be a more effective tool for fault feature representation. Second, coarse-grain process is firstly introduced to FWPDE. Novel feature named multi-scale FWPDE is developed, which can significantly improve fault diagnosis accuracy. Third, to select optimal feature set and optimize the hyperparameters of support vector machine (SVM) at the same time, a synchronous optimization strategy based on binary particle swarm optimization (BPSO) is presented, which can further improve the diagnosis accuracy. The superiority and effectiveness of the proposed method are verified by comparing to some existing fault diagnosis methods. The diagnosis accuracies of reverse-normal and normal-reverse switching processes reach 99.33% and 99.67%, respectively. Especially, the proposed method is suitable for diagnosis of similar faults, which can also provide reference for similar research fields.