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Rolling bearing fault diagnosis in strong noise background based on vibration signals

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Abstract

To solve the problem of difficulty in fault feature extraction for rolling bearings under strong noise conditions, a K-value calculation method of variational mode decomposition (VMD) based on singular value kurtosis difference spectrum is proposed, which is combined with the improved maximum correlation kurtosis deconvolution (MCKD) to achieve fault diagnosis. Firstly, the singular value decomposition (SVD) algorithm is used to denoise the strong noise rolling bearing fault signals, and then, the optimal number of decomposition layers is determined according to the center frequency distance between the singular value kurtosis spectrum and the decomposed intrinsic modal function (IMF). Filtering IMFs and reconstructing faulty signals by correlation and kurtosis criteria. Optimizing the filter length L and the number of shifts M of the MCKD using the dung beetle optimizer (DBO) to enhance the signal characteristics. Finally, the envelope spectrum is used to extract the eigenfrequencies for fault diagnosis of rolling bearings and to determine the fault location. Experimentally, it is shown that the method can effectively extract the fault characteristics of rolling bearings and carry out fault diagnosis under strong noise interference.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This research was funded by the National Natural Science Foundation of China (62203146).

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Authors and Affiliations

  1. Heilongjiang Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China

    Dongjie Li, Mingyue Li, Liu Yang, Xueying Wang, Fuyue Zhang & Yu Liang

  2. Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China

    Dongjie Li

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  1. Dongjie Li
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  2. Mingyue Li
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  3. Liu Yang
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  4. Xueying Wang
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  5. Fuyue Zhang
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  6. Yu Liang
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Contributions

ML is responsible for the entire experiment execution and paper writing. DL is responsible for the review and correction of papers. LY and XW are responsible for guiding the experiment. FZ and YL are responsible for checking and reviewing manuscripts. This study is funded by LY.

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Correspondence to Dongjie Li.

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Li, D., Li, M., Yang, L. et al. Rolling bearing fault diagnosis in strong noise background based on vibration signals. SIViP 18, 1295–1303 (2024). https://doi.org/10.1007/s11760-023-02846-y

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  • DOI: https://doi.org/10.1007/s11760-023-02846-y

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