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Fault diagnosis model of rolling bearing based on parameter adaptive AVMD algorithm

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Abstract

Aiming at the intrinsic aspect that the weak features of the early fault information of rolling bearings are not easy to extract, a parameter Adaptive Variational Modal Decomposition (AVMD) based algorithm is proposed for bearing fault signal feature extraction. Since the number of Variational Modal Decomposition (VMD) decomposition and penalty factor play an important role in VMD decomposition effect, the irregularities in the selection of these two influencing parameters are analyzed. We exploit the stronger global search capability of the improved sparrow search algorithm (LSSA) for adaptive parameter selection of the VMD algorithm. In this paper, the Levy flight algorithm is introduced and chaos is added to initialize sparrow population position to prevent sparrow from falling into the disadvantage of local optimum in the search process. In addition, this paper also combines the maximum kurtosis index, the minimum envelope entropy index and the number of iterations of VMD to form the objective function of the LSSA. The VMD algorithm with optimized parameters decomposes the signal to be measured, the decomposed IMFs was reconstructed, finally the validity of the model was verified by calculating 20 features (time domain and frequency domain) of the reconstructed signal as the input vector of the SVM classifier. Finally, the feasibility of this model in fault diagnosis of rolling bearing is verified using simulation and example experiments.

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Acknowledgments

This work was financially supported by the Natural Science Foundation of Shandong province under Grant ZR2020MF033, the project of National Bureau of Statistics of China (No.2019LZ10),the Project of National Natural Science Foundation of China (No. 61502280, No. 61472228). The General project of science and Technology Plan of Beijing Municipal Commission of Education (KM202010017001).

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Authors and Affiliations

  1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China

    Meixuan Li & Chun Yan

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Wei Liu

  3. Department of Mathematics and Physics, Beijing Institute of Petro-chemical Technolog, Beijing, 102617, China

    Xinhong Liu

  4. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Mengchao Zhang

  5. College of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Jiankai Xue

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  1. Meixuan Li
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  2. Chun Yan
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  3. Wei Liu
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  4. Xinhong Liu
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  5. Mengchao Zhang
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  6. Jiankai Xue
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Correspondence to Chun Yan or Wei Liu.

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Li, M., Yan, C., Liu, W. et al. Fault diagnosis model of rolling bearing based on parameter adaptive AVMD algorithm. Appl Intell 53, 3150–3165 (2023). https://doi.org/10.1007/s10489-022-03562-9

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