Enhanced Blind Separation of Rail Defect Signals with Time–Frequency Separation Neural Network and Smoothed Pseudo Wigner–Ville Distribution

Railways are crucial in economic development and improving people’s livelihoods. Therefore, defect detection and maintenance of rails are particularly important. In order to accurately separate and identify the rail defect from the mixed signals by acoustic emission (AE) techniques, this paper proposes a novel time–frequency separation neural network (TFSNN) architecture to solve the problems existing in the blind source separation (BSS), such as in non-stationary signals and low stability in the convergence. Combined with the smoothed pseudo Wigner–Ville distribution (SPWVD), this method can increase the spectrogram resolution, suppress the noise interference, and effectively improve the extraction performance of crack signals. In addition, 1D-CNN and GRU structures were introduced in the TFSNN structure to exploit the dominant features from AE signals. A dense regressor was also subsequently used to estimate the separation weights. Simulation and experiments showed that compared with traditional algorithms like independent component analysis, shallow neural networks, and time–frequency blind source separation, the proposed algorithm can provide better separation performance and higher stability in rail crack detection.