Damage Detection in Switch Rails via Machine Learning

Abstract

Switch rail is a weak but essential component of high-speed rail (HSR) systems. Due to aging and the potential of fatigue damage accumulation, it has an urgent requirement for damage detection. An automatic classification method of switch rail damage based on feature integration and machine learning is proposed. According to the characteristics of switch rail and guided wave, several features extracted from different signal processing domains (such as time domain, power spectrum domain and time-frequency domain) are proposed and defined to characterize the complexity of switch rail damage. A damage index is defined to eliminate the effects of various environmental and operational conditions. A feature selection method based on binary particle swarm optimization (BPSO) is proposed. This method uses a new fitness function to select the most damage-sensitive features, eliminate the irrelevant and redundant features, and improve the classification performance. The least-squares support-vector machine (LS-SVM) is adopted to build an automatic classification model to reduce the probability of artificial error diagnosis and improve the generalization ability. Finally, experiment on the switch rail foot is conducted to verify the proposed method. The results show that the method has the ability of damage identification, which is better than traditional methods.