Article citationsMore>>

Sun, Y. and Yu, J. (2022) Fault Feature Extraction of Rolling Bearings Using Local Mean Decomposition-Based Enhanced Sparse Coding Shrinkage. Journal of King Saud University-Engineering Sciences, 34, 17-22.
https://doi.org/10.1016/j.jksues.2021.11.011

has been cited by the following article:

• TITLE: Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine

  AUTHORS: Chenglong Yang, Yushu Lai, Qiuyue Li

  KEYWORDS: Electromagnetic Acoustic Emission Technology, LMD, LSSVM, Defect Detection of Metal Crack

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.5, May 29, 2023

  ABSTRACT: 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.