An Improved Rail Crack Classification Method Based on Wavelet Multi-Resolution Analysis and Signal Separability

Rail crack is a serious threat to the safety of train operations. Accurately classifying various types of rail crack is a crucial aspect in preventing potential accidents. To address this challenge, this paper proposes an improved rail crack classification method based on wavelet multi-resolution analysis (WMRA) and signal separability. By using electromagnetic acoustic emission (EMAE) technique, crack-related acoustic emission (AE) signals are generated, which are then decomposed into sub-signals using WMRA. Subsequently, the best feature set (BFS) is obtained from each sub-signal based on the constructed feature separability index (ST). The signal separability index (SSI) is constructed to evaluate the information quantity of each sub-signal for subsequent signal analysis. Finally, by leveraging the BFS and the most informative sub-signal, crack classification is performed. Comparative analysis with conventional approaches reveals a remarkable enhancement in the accuracy of rail crack classification, specifically within a reduced feature dimension. Noteworthy, this method demonstrates the frequency band concentration effect of EMAE, thereby providing valuable insights for further research in the field of rail crack classification.