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- Title
基于 CEEMDAN 多尺度熵和 SSA-SVM 的 滚动轴承故障诊断研究.
- Authors
李怡; 李焕修; 刘自然
- Abstract
Aiming at the problem that when support vector machine ( SVM) was applied to bearing fault classification, the traditional intelligent algorithm optimization of SVM parameters had problems such as slow optimization speed, more adjustment parameters, and easy to fall into local optimal values, a fault diagnosis method based on CEEMDAN and SSA-SVM was proposed. The fault feature extraction and SVM parameter optimization of rolling bearings were studied. A new swarm intelligence optimization algorithm sparrow search algorithm (SSA) was introduced to optimize the parameters of the SVM to improve the optimization speed and the accuracy of bearing fault classification. Firstly, the complete ensemble empirical mode decomposition with adaptive noise ( CEEMDAN) algorithm was used to decompose the signal to obtain several intrinsic mode functions ( IMF) . Then the correlation coefficient method was used to select the useful IMF components and recombine them. Finally, the multi-scale entropy of the reconstructed signal was calculated as the feature vector and inputted into the SVM optimized by SSA for fault classification. The results indicate that this method can accurately obtain fault information and has high recognition accuracy. Comparing with SVM optimized by PSO and GA, t his method has better fault diagnosis and classification performance.
- Publication
Journal of Mechanical & Electrical Engineering, 2021, Vol 38, Issue 5, p599
- ISSN
1001-4551
- Publication type
Article
- DOI
10.3969/j.issn.1001-4551.2021.05.012