Abstract
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.
Highlights
As critical components in rotating machinery, bearings that are not in a good condition can cause frequent machinery breakdowns [1], and these faults may result in equipment instability, poor efficiency, and even major production-safety accidents [2]
A carried series ofout experiments were performed to verify the performance of the series of experiments were performed to verify the performance of the reconstruction methods and the block sparse Bayesian learning (BSBL)-Expectation Maximization (EM) was compared with some typical recovery algorithms
This paper proposed a bearing-condition monitoring method for wireless sensor network (WSN) using BSBL based on Compressed Sensing, which could reduce the burden on the WSN nodes
Summary
As critical components in rotating machinery, bearings that are not in a good condition can cause frequent machinery breakdowns [1], and these faults may result in equipment instability, poor efficiency, and even major production-safety accidents [2]. A serious problem is that almost all these methods require collecting original vibration signals according to sample theorem, and applying complex algorithms to compress data in node processers. This processing does not reduce the workload. BSBL outperforms the traditional CS algorithms and has the capacity to recover non-sparse signals with high precision It has been successfully applied in the monitoring of fetal electrocardiogram (FECG) and electroencephalogram (EEG) via wireless body-area networks [21]. Based on the properties of the bearing vibration signals and related CS theory, this paper proposed a new reconstruction method combining BSBL and sparsity in transform domain to improve the recovery accuracy. We analyze the reconstruction performances and compare the results of BSBL with other methods, and discuss the influences of the block sizes and signal-noise ratio
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