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An Integrated Method Based on Sparrow Search Algorithm Improved Variational Mode Decomposition and Support Vector Machine for Fault Diagnosis of Rolling Bearing

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Abstract

Purpose

The main purpose of this paper is to reduce the influence of penalty parameter (\(\alpha\)) and mode number K on VMD decomposition. Furthermore, the optimized VMD is combined with support vector machine (SVM) to diagnose rolling bearing faults.

Methods

This paper proposed a parameter adaptive VMD method based on the sparrow search algorithm (SSA), which is named SSA-VMD. First, the minimum mean envelope entropy (MEE) as the objective function to find the best parameters combination. Then, VMD with the optimized parameters is used to decompose the signals and obtain the corresponding components. In order to extract fault features better, the fuzzy entropy (FE) of intrinsic mode functions (IMFs) which are attained by SSA-VMD as feature vectors. Finally, input the feature vectors into the support vector machine which is combined with the SSA to improve accuracy, so as to get the classification result.

Results

Two public data sets are used to verify the effectiveness of SSA-VMD-SVM. The experimental results show that this method is faster than other methods in decomposition speed and has better performance in fault classification of rolling bearings.

Conclusion

In this paper, a method for adaptive selection of VMD parameters is proposed, and combined with SVM, a method for fault diagnosis of rolling bearings is formed. At the same time, the method can distinguish single fault and mixed fault well, and has a good classification effect. This result helps to provide a new fault diagnosis method.

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Acknowledgements

This work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2019JJ50624). The Research Foundation of Education Department of Hunan Province, China (Grant No. 20B567), and the National Natural Science Foundation of China (Grant No. 62071411).

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    Authors and Affiliations

    1. School of Automation and Electronic Information, Xiangtan University, North Second Ring Road, Xiangtan, 411105, Hunan Province, People’s Republic of China

      Mengjiao Wang, Wenjie Wang & Yibing Zhang

    2. School of Physics and Optoelectronics, Xiangtan University, North Second Ring Road, Xiangtan, 411105, Hunan Province, People’s Republic of China

      Jinfang Zeng

    Author notes

    1. Wenjie Wang, Jinfang Zeng and Yibing Zhang have contributed equally to this work.

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      1. Mengjiao Wang
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      Correspondence to Mengjiao Wang.

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      Wang, M., Wang, W., Zeng, J. et al. An Integrated Method Based on Sparrow Search Algorithm Improved Variational Mode Decomposition and Support Vector Machine for Fault Diagnosis of Rolling Bearing. J. Vib. Eng. Technol. 10, 2893–2904 (2022). https://doi.org/10.1007/s42417-022-00525-9

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      • DOI: https://doi.org/10.1007/s42417-022-00525-9

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