Research on the Application of AI in Acoustic Emission Waveform Data Pattern Recognition

This paper investigates the method of using the deep learning model ECAPA-TDNN for pattern recognition in acoustic emission (AE) waveform data. Acoustic emission technology is a crucial non-destructive testing method. Traditional analysis methods mainly rely on parameter data, while waveform data contain more information, which can significantly enhance recognition accuracy. The ECAPA-TDNN model, by incorporating SE-Res2Block modules, multi-layer feature aggregation and summation, and an attention pooling mechanism, can effectively capture complex temporal features, enabling accurate classification of AE events such as lead break, friction, impact, and steel ball drop. The experiments, accelerated by GPU, combined with data preprocessing and data augmentation techniques, significantly improved training efficiency and recognition accuracy. The results show that the overall recognition accuracy on the test set reached 93.7%, validating the effectiveness and superiority of this method in AE waveform data analysis. This research provides new insights and tools for the development of non-destructive testing technology, and also lays a foundation for future model optimization and application expansion.