Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine
Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine作者机构：School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing China Chongqing Engineering Technology Research Center for Light Alloy and Processing Chongqing China
出 版 物：《Journal of Computer and Communications》 (电脑和通信（英文）)
年 卷 期：2023年第11卷第5期
页 面：70-83页
学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
主 题：Electromagnetic Acoustic Emission Technology LMD LSSVM Defect Detection of Metal Crack
摘 要：Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.
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