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Laplace Prior-Based Bayesian Compressive Sensing Using K-SVD for Vibration Signal Transmission and Fault Detection

1
Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
2
Radar Sergeant School, Air Force Early Warning Academy, Wuhan 430019, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 517; https://doi.org/10.3390/electronics8050517
Received: 3 April 2019 / Revised: 30 April 2019 / Accepted: 1 May 2019 / Published: 9 May 2019
(This article belongs to the Section Systems & Control Engineering)
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Abstract

Vibration signal transmission plays a fundamental role in equipment prognostics and health management. However, long-term condition monitoring requires signal compression before transmission because of the high sampling frequency. In this paper, an efficient Bayesian compressive sensing algorithm is proposed. The contribution is explicitly decomposed into two components: a multitask scenario and a Laplace prior-based hierarchical model. This combination makes full use of the sparse promotion under Laplace priors and the correlation between sparse blocks to improve the efficiency. Moreover, a K-singular value decomposition (K-SVD) dictionary learning method is used to find the best sparse representation of the signal. Simulation results show that the Laplace prior-based reconstruction performs better than typical algorithms. The comparison between a fixed dictionary and learning dictionary also illustrates the advantage of the K-SVD method. Finally, a fault detection case of a reconstructed signal is analyzed. The effectiveness of the proposed method is validated by simulation and experimental tests. View Full-Text
Keywords: vibration signal; Bayesian compressive sensing; K-SVD; gearbox; bearing vibration signal; Bayesian compressive sensing; K-SVD; gearbox; bearing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ma, Y.; Jia, X.; Hu, Q.; Xu, D.; Guo, C.; Wang, Q.; Wang, S. Laplace Prior-Based Bayesian Compressive Sensing Using K-SVD for Vibration Signal Transmission and Fault Detection. Electronics 2019, 8, 517.

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