Crack Pattern Recognition Based on Acoustic Emission Waveform Features

To better identify the crack propagation pattern of coal or rock sample during loading, such as the initiation of new cracks or the propagation of pre-existing cracks. Acoustic emission (AE) characteristics of coal sample were analyzed to illustrate the correlation between AE frequency-domain information and crack propagation pattern. By introducing unsupervised and supervised algorithms to classify AE signals caused by crack pattern in different damage stages, and compare with the experimental damage. The results show that AE peak frequency, centroid frequency and weight frequency can be used to distinguish the initiation of new cracks or the propagation of pre-existing cracks. Fuzzy C-Means (FCM) unsupervised method was used to cluster AE signals generated by specific crack propagation pattern, and the maximum error was 3.8%. In addition, we compared the performance of Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Random Forest (RF) algorithms, the accuracy of RF was higher than ELM, and the running time was significantly 20 times lower than SVM. After expanding the application of RF, it was consistent with the experimental observation. This study contributes to evaluate the crack initiation, propagation and location of engineering structures, such as mine coal pillar and bridge.