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

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