Sun Jiedi, Yu Yang, Wen Jiangtao
School of Information Science and Engineering, Yanshan University, 438, Hebei Avenue, Qinhuangdao 066004, China.
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
Sensors (Basel). 2017 Jun 21;17(6):1454. doi: 10.3390/s17061454.
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.
利用无线传感器网络（WSN）对轴承状态进行远程监测是工业领域的一个发展趋势。在复杂的工业环境中，无线传感器网络面临三个主要限制：能量低、内存少和运算能力低。传统的数据压缩方法仅专注于数据压缩，无法克服这些限制。针对这些问题，本文提出了一种基于压缩感知（CS）的压缩数据采集与重构方案，压缩感知是一种新颖的信号处理技术，并将其应用于通过无线传感器网络进行的轴承状态监测。压缩数据采集通过投影变换实现，可大大减少节点需要处理和传输的数据量。原始信号的重构在主机中通过复杂算法完成。轴承振动信号不仅具有稀疏特性，还具有特定结构。本文介绍了块稀疏贝叶斯学习（BSBL）算法，该算法通过利用信号的块特性和固有结构来重构变换域的压缩感知稀疏系数，进而恢复原始信号。通过使用块稀疏贝叶斯学习算法，可显著改善压缩感知重构。实验与分析表明，块稀疏贝叶斯学习方法具有良好的性能，适用于实际的轴承状态监测。
