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Roller bearing fault diagnosis based on LMD and multi-scale symbolic dynamic information entropy

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Abstract

This paper presents a new fault feature extraction method based on the combination of local mean decomposition (LMD) and multi-scale symbolic dynamic information entropy (MSDE). The LMD method decomposes the multi-component signal into a finite number of product functions (PFs) to extract the characteristic information of the original signal. In this paper, the comparison of MSDE and the multi-scale sample entropy shows that the symbolic dynamic information entropy (SDE) has simple calculation and has better stability than sample entropy at multiscale. Entropy values of PFs that combine LMD and MSDE are extracted as the feature sets, which are inputted into the affinity propagation clustering model to identify the fault types and fault degree of roller bearings. This paper also discusses the influence of different filtering algorithms on the noise removal of bearing signal. Application results demonstrate the effectiveness of the proposed fault diagnosis method.

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Acknowledgments

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. School of Reliability and Systems Engineering, Beihang University, Beijing, China

    Minghong Han, Yaman Wu, Yumin Wang & Wei Liu

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  1. Minghong Han
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  2. Yaman Wu
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  3. Yumin Wang
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  4. Wei Liu
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Corresponding author

Correspondence to Minghong Han.

Additional information

Minghong Han obtained his doctoral degree from Beihang University in 2004. He is currently a Master supervisor in the School of Reliability and Systems Engineering of Beihang University. His research interests include fault diagnosis, prediction and health management, and multidisciplinary design optimization.

Yaman Wu is a Master's degree student in the School of Reliability and Systems Engineering of Beihang University. Her research interests include fault diagnosis and multidisciplinary design optimization.

Yumin Wang is a Master's degree student in the School of Reliability and Systems Engineering of Beihang University. Her research interests include fault diagnosis and health management.

Wei Liu is a Master's degree graduate of the School of Reliability and Systems Engineering of Beihang University. His research interests include fault diagnosis and integrated software platform for the design optimization of complex system.

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Han, M., Wu, Y., Wang, Y. et al. Roller bearing fault diagnosis based on LMD and multi-scale symbolic dynamic information entropy. J Mech Sci Technol 35, 1993–2005 (2021). https://doi.org/10.1007/s12206-021-0417-3

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  • DOI: https://doi.org/10.1007/s12206-021-0417-3

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