期刊
APPLIED SCIENCES-BASEL
卷 7, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/app7101004
关键词
fault diagnosis; rolling bearing; variational mode decomposition; approximate entropy; kernel extreme learning machine
类别
资金
- National Natural Science Foundation of China [51775243, 51675035]
- Key Project of Industry Foresight and Common Key Technologies of Science and Technology Department of Jiangsu Province [BE2017002-2]
- Fundamental Research Funds for the Central Universities [JUSRP51732B]
Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据