Intelligent Fault Diagnosis of Rotating Machinery Using ICD and Generalized Composite Multi-Scale Fuzzy Entropy

A new intelligent fault diagnosis algorithm of rotating machinery based on intrinsic characteristic-scale decomposition (ICD), generalized composite multi-scale fuzzy entropy (GCMFE), Laplacian score (LS), and particle swarm optimization-based support vector machine (PSO-SVM) is proposed in this paper. First, ICD is applied to decompose a vibration signal into a sum of product components. Second, GCMFE is proposed to evaluate the complexity of the decomposed vibration signals. GCMFE can overcome the drawbacks of the MFE method, and the superiority of GCMFE is validated using a simulated signal. Third, the LS method is utilized to select the extracted fault features. In the end, the selected features are input into the PSO-SVM to classify different health conditions. The simulated and experimental results validate the superiority of the proposed method in fault feature extraction compared with three other methods: ICD-MFE, ICD-CMFE, and GCMFE.