A New Bearing Fault Diagnosis Method based on Fine-to-Coarse Multiscale Permutation Entropy, Laplacian Score and SVM.pdf (2.61 MB)
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A new bearing fault diagnosis method based on fine-to-coarse multiscale permutation entropy, Laplacian score and SVM

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journal contribution
posted on 04.08.2021, 09:17 by Zhiqiang Huo, Eve ZhangEve Zhang, Lei Shu, Michael Gallimore
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 the wavelet packet decomposition. The entropy measure estimates the complexity 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 the synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract the 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 the bearing fault patterns under different fault states and severity levels.


International and Hong Kong, Macao and Taiwan Collaborative Innovation Platform and Major International Cooperation Projects of Colleges in Guangdong Province under Grant 2015KGJHZ026

Natural Science Foundation of Guangdong Province under Grant 2016A030307029



  • Aeronautical, Automotive, Chemical and Materials Engineering


  • Aeronautical and Automotive Engineering

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IEEE Access




17050 - 17066


Institute of Electrical and Electronics Engineers (IEEE)


AM (Accepted Manuscript)

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Dr Eve Zhang. Deposit date: 29 July 2021