Wood acoustic emission signals classification based on pseudospectrum, and entropy

Abstract The nondestructive testing technology of generated acoustic emission(AE) signals for wood is of great significance for the evaluation of internal damages of wood. In order to improve the classification accuracy and adaptability of AE signal, we selected two features(pseudospectrum, entropy) for classify AE signals in the process of wood fracture using SVM classifier. The three-point bending load damage experiment was utilized to generate original AE signals. Evaluation indexes(Precision, Accuracy, Recall, F1-score, Cohen Kappa score, Matthews Corrcoef) were adopted to assess the classification model. The results showed that the overall accuracy of the SVM classification model obtained by the method combining pseudospectrum and entropy features is 89.44%, which indicates that this automatic classification model has good AE signal recognition performance.