Local track irregularity identification based on multi-sensor time-frequency features of high-speed railway bridge accelerations

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 multis-sensor time-frequency features of high-speed railway bridge accelerations. Continuous wavelet transform (CWT) is used to analyze the multi-sensor accelerations of railway bridges. Moreover, time-frequency features based on the sum of wavelet coefficients are proposed, considering the influence of the distance from the measurement points to the local irregularity on the recognition accuracy. Then, the multi-domain features are utilized to recognize deteriorated railway locations. A simply-supported high-speed railway bridge traversed by a railway train is adopted as a numerical simulation. Comparative studies are 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.