Fast adaptive time-varying window length STFT for compound track short-wave defects identification

Track short-wave defect, as a common defect of railway tracks, can cause dynamic response on both vehicle and its components and thus severely affect the stability and safety of running vehicle. The vibration signal, collected by an accelerator mounted on the axle box of vehicle, is a kind of vehicle dynamic response signal. It consists of complex periodic and impact signal components, which poses challenges for track short-wave defects identification. In this paper, the forms and features of vibration signal of vehicle dynamic response are analyzed at first. Then, based on short-time Fourier transform (STFT), a fast adaptive time-varying window length STFT (FATW-STFT) is developed for compound track short-wave defect analysis. Specifically, by quickly calculating a series of time-frequency planes with various window length, modified kurtosis, boundary function, and Rényi entropy are introduced to sift the time-frequency planes and optimize the window lengths at each time point. Unlike other STFT-based methods, FATW-STFT uses fully adaptive window length optimization to distinguish both periodic and impactive signal components, which helps to detect multiform track short-wave defects such as corrugation and impact of welded joint. Both the simulated and measured results indicate that the proposed approach can rapidly and effectively identify the compound track short-wave defects and possesses potential prospect of practical applications.