Feature extraction, recognition, and classification of acoustic emission waveform signal of coal rock sample under uniaxial compression

In this study, based on Mel frequency cepstrum coefficient (MFCC) method, the AE signal characteristics of coal and rock samples were extracted, and the stress state criterion based on signal features was constructed. By integrating back propagation (BP) neural network for deep learning of signal characteristics, the recognition, classification, and prediction of coal and rock materials were realized. The results show that the MFCC could characterize the variation law of the original signal, with the sharp fluctuation of the amplitudes of both the AE signal and MFCC when the rock stress was near the peak value. Considering the ratio of sample stress to peak stress as the stress state, the correlation between MFCC and stress state was analyzed. The BP neural network exhibited a high accuracy rate for the signal characteristics represented by MFCC, achieving an accuracy of more than 95% with a fast recognition speed. Notably, the evaluation results of neural network model were stable and reliable. Therefore, MFCC can be used to extract the AE waveform signal characteristics and evaluate the stability of stress state for coal and rock materials. The recognition, classification, and prediction of high-precision results of the two types of waveform characteristics of coal and rock can be achieved through BP neural network.