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Early bearing fault diagnosis based on the improved singular value decomposition method

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Abstract

The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed, and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.

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The data used in this paper are all owned by the lab of the research group. As the research is still continuing, the data involved in this paper is not publicly available.

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The algorithm involved in this paper is still being studied by the research group, so it is not publicly disclosed.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 52075008. The authors are grateful to the editors and anonymous reviewers for their helpful comments and constructive suggestions.

Funding

This research is funded by the National Natural Science Foundation of China (Grant No. 52075008).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Chao Yang District, Beijing, 100124, China

    Lingli Cui, Mengxin Sun & Chunqing Zha

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  1. Lingli Cui
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  2. Mengxin Sun
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  3. Chunqing Zha
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Contributions

MXS analyzed the data. LLC and CQZ provided guidance and recommendations for the research. MXS contributed to the contents and writing of the manuscript. All authors have read and approved the final manuscript.

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Correspondence to Chunqing Zha.

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Cui, L., Sun, M. & Zha, C. Early bearing fault diagnosis based on the improved singular value decomposition method. Int J Adv Manuf Technol 124, 3899–3910 (2023). https://doi.org/10.1007/s00170-021-08237-2

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  • DOI: https://doi.org/10.1007/s00170-021-08237-2

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