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The double-feature extraction method based on slope entropy and symbolic dynamic entropy for the fault diagnosis of rolling bearing

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Abstract

This paper explores the application of slope entropy in fault diagnosis. In order to improve the recognition rate of faults, double-feature extraction is proposed, which combines slope entropy (SLEn) and symbolic dynamic entropy (SDE). There are five methods of entropy (slope entropy, permutation entropy, symbolic dynamic entropy, and sample entropy) are combined two by two in the double-feature experimental extraction. The K-Nearest Neighbor (KNN) is selected as the recognizer to calculate their recognition rates. The slope entropy and symbolic dynamic entropy having the highest recognition rates at 100%. It is followed by the combination of slope entropy and permutation entropy with an identification rate of 99%. In order to compensate for the lack of adaptive parameter selection in slope entropy and divide the parameter α and β ranges of slope entropy more effectively, a new criterion is proposed to optimize the slope entropy by using the sparrow search algorithm (SSA-SLEn). Its state pattern reflects the characteristics of the fault signals in a more reasonable way.

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Acknowledgements

Thanks to Professor Yuxuan Han for providing financial support.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2023-JC-YB-426).

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Authors and Affiliations

  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, Shaanxi, China

    Zhe Zhang, Yingwei Liu, Pengfei Huangfu, Zhiyuan Ma, Weichen Shi & Ke Feng

  2. School of Automation, Xi’an University of Posts & Telecommunications, Xi’an, Shaanxi, China

    Yuxuan Han

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  1. Zhe Zhang
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  2. Yingwei Liu
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Contributions

ZZ completed the experimental and algorithm writing of the research paper, YL reviewed and proofread the manuscript, YH provided the fund support, and the other authors, respectively, drew the experimental data and corrected the grammar.

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Correspondence to Yuxuan Han.

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Zhang, Z., Liu, Y., Han, Y. et al. The double-feature extraction method based on slope entropy and symbolic dynamic entropy for the fault diagnosis of rolling bearing. SIViP (2024). https://doi.org/10.1007/s11760-024-03144-x

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