Abstract

Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), Laplacian Score (LS) and Support Vector Machine (SVM). A novel entropy measure, named F2CMPE, was proposed by calculating permutation entropy via multiple-scale fine-grained and coarse-grained signals based on wavelet packet decomposition. The entropy measure estimates the dynamic changes of time series from both low- and high-frequency components. Moreover, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in the conventional Composite Multiscale Permutation Entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract entropy-based features from bearing vibration signals. Then, LS and SVM are used for selection of features and fault classification respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the capability of the proposed method to recognize and identify bearing fault patterns under different fault states and severity levels.