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Article

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

1
School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 430000, China
2
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5541; https://doi.org/10.3390/s20195541
Submission received: 21 August 2020 / Revised: 22 September 2020 / Accepted: 23 September 2020 / Published: 27 September 2020
(This article belongs to the Special Issue Data Acquisition and Processing for Fault Diagnosis)

Abstract

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.
Keywords: simultaneously low rank and group sparse; Hankel matrix; singular value decomposition; periodic information index; bearing fault diagnosis simultaneously low rank and group sparse; Hankel matrix; singular value decomposition; periodic information index; bearing fault diagnosis

Share and Cite

MDPI and ACS Style

Zheng, K.; Bai, Y.; Xiong, J.; Tan, F.; Yang, D.; Zhang, Y. Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis. Sensors 2020, 20, 5541. https://doi.org/10.3390/s20195541

AMA Style

Zheng K, Bai Y, Xiong J, Tan F, Yang D, Zhang Y. Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis. Sensors. 2020; 20(19):5541. https://doi.org/10.3390/s20195541

Chicago/Turabian Style

Zheng, Kai, Yin Bai, Jingfeng Xiong, Feng Tan, Dewei Yang, and Yi Zhang. 2020. "Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis" Sensors 20, no. 19: 5541. https://doi.org/10.3390/s20195541

APA Style

Zheng, K., Bai, Y., Xiong, J., Tan, F., Yang, D., & Zhang, Y. (2020). Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis. Sensors, 20(19), 5541. https://doi.org/10.3390/s20195541

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