A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals. This method leverages correlation coefficient filtering to extract the primary components, followed by classification and recognition using the Swin Transformer neural network. The results demonstrate that the improved CELMD method effectively extracts the main features of the acoustic emission signals with higher decomposition accuracy and reduced occurrences of mode mixing and end effects. Furthermore, the Swin Transformer neural network exhibits outstanding performance in classifying acoustic emission signals, surpassing both convolutional neural networks and ViT neural networks in terms of accuracy and convergence speed. Moreover, utilizing preprocessed data from the improved CELMD enhances the performance of the Swin Transformer neural network. With an increase in data volume, the accuracy, stability, and convergence speed of the Swin Transformer neural network continuously improve, and using preprocessed data from the enhanced CELMD yields superior training results compared to those obtained without preprocessing.