Recognition of shear and tension signals based on acoustic emission parameters and waveform using machine learning methods

Acoustic emission (AE) technology is widely used to monitor the damage evolution of rock. Identifying AE signals is crucial to reveal the rock cracking mechanism. The current tension and shear signal discrimination methods primarily rely on AE parameters. However, the differences between AE parameters and waveform images used as inputs require further exploration. Acoustic emission data from Brazilian splitting and direct shear tests of red sandstone were collected. A decision tree was applied to classify the signals based on the AE parameters: Rise time (RT), Counts, Amplitude, Peak frequency (PF), and absolute energy. Three typical CNN-based networks (InceptionV3, ResNet50, and VGG19) were used to classify the AE signals using images of waveforms, reconfigured waveforms, and spectrograms generated by wavelet packets. The results showed that using AE parameters as input to distinguish tensile and shear signals yields an accuracy of up to 88%, increasing recognition accuracy when using PF as input. In addition, increasing the number of input parameters to the decision tree can improve average recognition accuracy, with single, double, and three parameters being 65%, 78%, and 82%, respectively. However, when the number of parameters exceeds three, the accuracy improvement is minimal, with four and five parameters being 84% and 86%, respectively. The CNN-based networks outperform the decision tree in terms of recognition accuracy. It is recommended that the VGG19 network and waveform are adopted as input to identify the tension and shear AE signals. In contrast, using the spectrogram as input enhances the stability of the model, particularly for the validation set, although the improvement is limited.