Open AccessWood acoustic emission signals classification based on pseudospectrum, and entropy
Author(s) -
Meilin Zhang,
Junqiu Li,
Jiale Xu,
Jingjing Zheng,
Qinghui Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012048
Subject(s) - acoustic emission , support vector machine , pattern recognition (psychology) , artificial intelligence , classifier (uml) , entropy (arrow of time) , computer science , speech recognition , acoustics , physics , quantum mechanics
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.