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Initial fault diagnosis of bearing based on AVMD-SE and multiscale enhanced morphological top-hat filter

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Abstract

Early fault signature detection and background noise removal are essential for bearing fault diagnosis. A novel multiscale enhanced morphological top-hat filter fault diagnosis method, adaptive variational mode decomposition-sample entropy-multiscale enhanced top-hat filter (AVMD-SE-MEMTF), is proposed based on AVMD-SE noise reduction. First, gray wolf optimization algorithm is proposed to optimize the VMD to achieve the optimal decomposition parameters adaptively and combine with SE to eliminate the high noise components and improve the noise reduction effect. Then, based on the pulse extraction property of morphological operations, the concept of MEMTF is proposed. To enhance the multiscale index selection strategy, a synthesis method of eigenfrequency envelope coefficients is constructed to increase the accuracy of the operator during the vibration signal process. Finally, experimental and engineering results show that the proposed method has good diagnostic performance for weak faults in the presence of noise interference.

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Abbreviations

AVMD :

Adaptive variational mode decomposition

GWO :

Gray wolf optimization

PSO :

Particle swarm optimization

MEMTF :

Multiscale enhanced top-hat filter

CE :

Characteristic envelope

IMF :

Intrinsic mode function

EMD :

Empirical mode decomposition

SE :

Sample entropy

CFIC :

Characteristic frequency intensity coefficient

PCA :

Principal component analysis

ESS :

Envelope sparseness

SK :

Spectral kurtosis

MG :

Morphological gradient

WTH :

White top-hat

DIF :

Difference filter

BTH :

Black top-hat

CMFH :

Combination morphological filter-hat

ACDIF :

Average combination difference morphological filter

ACMH :

Average of a combined hat

AEDH :

Average of erosion and dilation hat

AVGH :

Average of opening and closing hat

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Numbers 51675350 and 51705 337).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang University of Technology, Shenyang, 110870, China

    Tong Wang, Changzheng Chen, Yuanqing Luo & Siyu Zhao

  2. BMW Brilliance Automotive Ltd., Shenyang, 110143, China

    Tong Wang & Shaohui Huang

  3. Liaoning Engineering Center for Vibration and Noise Control, Shenyang, 110870, China

    Changzheng Chen

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  1. Tong Wang
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  2. Changzheng Chen
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  3. Yuanqing Luo
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  4. Siyu Zhao
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Corresponding author

Correspondence to Changzheng Chen.

Additional information

Tong Wang received his M.S. in Mechanical Engineering from the Shenyang University of Technology, Shenyang, China, in 2017. He is currently a Ph.D. student jointly trained by the School of Mechanical Engineering of the Shenyang University of Technology and BMW Brilliance Automotive Ltd., Shenyang, China. His main research interests are in product line condition monitoring and fault diagnosis.

Changzheng Chen is currently a Professor and a Ph.D. Supervisor with the School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China. He is currently the executive director of the fault diagnosis committee of the Chinese Society of Vibration Engineering. His current research interests include vibration, noise, and fault diagnosis.

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Wang, T., Chen, C., Luo, Y. et al. Initial fault diagnosis of bearing based on AVMD-SE and multiscale enhanced morphological top-hat filter. J Mech Sci Technol 36, 6289–6305 (2022). https://doi.org/10.1007/s12206-022-1141-3

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  • DOI: https://doi.org/10.1007/s12206-022-1141-3

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