Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization

Symmetry

Guang-Lin Zhuo¹  Chun-Yao Lee¹ 

[1] Department of Electrical Engineering, Chung Yuan Christian University, No. 200, Zhongbei Road, Zhongli District, Taoyuan City 320, Taiwan;

关键词: rotor;    fault diagnosis;    local mean decomposition;    wavelet packet decomposition;    chicken swarm optimization;    feature selection;   

DOI  :  10.3390/sym13030487

来源: DOAJ

【 摘 要 】

This article proposes an effective rotor fault diagnosis model of an induction motor (IM) based on local mean decomposition (LMD) and wavelet packet decomposition (WPD)-based multilayer signal analysis and hybrid genetic binary chicken swarm optimization (HGBCSO) for feature selection. Based on the multilayer signal analysis, this technique can reduce the dimension of raw data, extract potential features, and remove background noise. To compare the validity of the proposed HGBCSO method, three well-known evolutionary algorithms are adopted, including binary-particle swarm optimization (BPSO), binary-bat algorithm (BBA), and binary-chicken swarm optimization (BCSO). In addition, the robustness of three classifiers including the decision tree (DT), support vector machine (SVM), and naive Bayes (NB) was compared to select the best model to detect the rotor bar fault. The results showed that the proposed HGBCSO algorithm can obtain better global exploration ability and a lower number of selected features than other evolutionary algorithms that are adopted in this research. In conclusion, the proposed model can reduce the dimension of raw data and achieve high robustness.

【 授权许可】

Unknown