A Sound-Based Fault Diagnosis Method for Railway Point Machines Based on Two-Stage Feature Selection Strategy and Ensemble Classifier

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

Automated summary

This study introduced a sound-based fault diagnosis method for railway point machines, achieving over 99% diagnosis accuracy through feature selection and ensemble classifier optimization.