Clustering analysis of acoustic emission signals based on unsupervised deep learning

Based on acoustic emission (AE) waveform, this paper proposes a method of AE signal classification based on deep neural network and cluster analysis. In order to avoid the difficulty of making AE signal label manually, a large number of measured AE waveforms are used for deep one-dimensional convolutional autoencoder training in an unsupervised learning method to realize the automatic extraction of AE signal characteristics, and then the k-means clustering algorithm is combined to accurately distinguish different types of AE signals. The AE experiments of sudden fracture and friction of lead core on composite plates show that the proposed method can automatically identify different types of AE signals, and the identification accuracy is higher than the clustering method based on the artificial characteristics of AE signals.