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Open AccessArticle

Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring

1
School of Information Science and Engineering, Yanshan University, 438, Hebei Avenue, Qinhuangdao 066004, China
2
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(6), 1454; https://doi.org/10.3390/s17061454
Received: 24 March 2017 / Revised: 10 June 2017 / Accepted: 15 June 2017 / Published: 21 June 2017
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression only, cannot overcome these limitations. Aiming at these problems, this paper proposed a compressed data acquisition and reconstruction scheme based on Compressed Sensing (CS) which is a novel signal-processing technique and applied it for bearing conditions monitoring via WSN. The compressed data acquisition is realized by projection transformation and can greatly reduce the data volume, which needs the nodes to process and transmit. The reconstruction of original signals is achieved in the host computer by complicated algorithms. The bearing vibration signals not only exhibit the sparsity property, but also have specific structures. This paper introduced the block sparse Bayesian learning (BSBL) algorithm which works by utilizing the block property and inherent structures of signals to reconstruct CS sparsity coefficients of transform domains and further recover the original signals. By using the BSBL, CS reconstruction can be improved remarkably. Experiments and analyses showed that BSBL method has good performance and is suitable for practical bearing-condition monitoring. View Full-Text
Keywords: compressed sensing reconstruction; sparse Bayesian learning; block sparse structure; bearing condition monitoring; wireless sensor network compressed sensing reconstruction; sparse Bayesian learning; block sparse structure; bearing condition monitoring; wireless sensor network
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Sun, J.; Yu, Y.; Wen, J. Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring. Sensors 2017, 17, 1454.

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