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Roller Bearing Fault Diagnosis Based on Integrated Fault Feature and SVM

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

The purpose of this paper is to provide high accuracy and rapid fault detection simultaneously using integrated fault features and support vector machine.

Methods

This paper first proposes a new fault feature extraction approach that separates the signals of integrated fault features (IFF) rapidly. The singular values are obtained by singular value decomposition (SVD) of Hilbert spectrum which is attained by intrinsic mode functions (IMFs) through empirical mode decomposition (EMD), and then combined with the permutation entropy (PE) of signal to form the IFF vector. Next, the support vector machine (SVM) is proposed as the classifier to further enhance the fault diagnosis performance. Particle swarm optimization (PSO) is employed in this paper to optimally tune the parameters of SVM.

Results

On two public data platforms, the classification accuracy of IFF with SVM can reach 98.1% and 99.43%, which is 19.7% and 9.4% higher than that of single feature value with SVM at most

Conclusion

In this paper, a novel IFF extraction method has been proposed to improve the computational efficiency and accuracy of fault diagnosis for roller bearings. At the same time, the proposed method has good classification capability for various types of roller bearings and different sample number. This result is helpful to provide a new way of feature vector selection.

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Acknowledgements

This work was supported financially 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 National Natural Science Foundation of China (Grant No. 62071411). This work was also funded by China Scholarship Council (Grant No. 201808430258).

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Authors and Affiliations

  1. School of Automation and Electronic Information, Xiangtan University, Xiangtan, 411105, People’s Republic of China

    Mengjiao Wang, Yangfan Chen, Zhijun Li & Minglin Ma

  2. School of Electrical, Electronic and Computer Engineering, University of Western Australia, Crawley, 6009, Australia

    Xinan Zhang, Tat Kei Chau, Herbert Ho Ching Iu & Tyrone Fernando

Authors
  1. Mengjiao Wang
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  2. Yangfan Chen
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  3. Xinan Zhang
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  4. Tat Kei Chau
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  5. Herbert Ho Ching Iu
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  6. Tyrone Fernando
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  7. Zhijun Li
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  8. Minglin Ma
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Correspondence to Mengjiao Wang.

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Wang, M., Chen, Y., Zhang, X. et al. Roller Bearing Fault Diagnosis Based on Integrated Fault Feature and SVM. J. Vib. Eng. Technol. 10, 853–862 (2022). https://doi.org/10.1007/s42417-021-00414-7

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  • DOI: https://doi.org/10.1007/s42417-021-00414-7

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