LAUSR

Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train–track–bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities based on the multi-sensor time–frequency features of high-speed railway bridge accelerations. Continuous wavelet transform (CWT) was used to analyze the multi-sensor accelerations of railway bridges. Moreover, time–frequency features based on the sum of wavelet coefficients were proposed, considering the influence of the distance from the measurement points to the local irregularity on the recognition accuracy. Then, the multi-domain features were utilized to recognize deteriorated railway locations. A simply-supported high-speed railway bridge traversed by a railway train was adopted as a numerical simulation. Comparative studies were conducted to investigate the influence of vehicle speeds and the location of local track irregularity on the algorithm. Numerical simulation results show that the proposed algorithm can detect and locate local track irregularity accurately and is robust to vehicle speeds.