Compressed Sensing reconstruction forrotating machinery vibration signals based on the wavelet packet dictionary optimization
WEN Jiangtao 1 SUN Jiedi 2, YU Yang1 YAN Changhong1
1.Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004, China;
2.School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
Abstract:Machinery condition monitoring systems using industrial wireless sensor networks need complicated data compression and high-precisionreconstruction,however, thereare some limitations in the node resources of wireless sensor networks.A compressed sensing reconstruction method for rotating machinery vibration signals was proposed based on the wavelet packet dictionary optimization.Combining the multiresolution analysis with the K-SVD dictionary training, the wavelet packet dictionary optimization was introduced to replace the traditional sparse transformation method based on the orthogonal basis dictionary for improving the signal sparseness.According to the rotating machinery vibration signal characteristics, a block sparse Bayesian learning framework was put forward in which the expectation-maximization method was applied instead of the common reconstruction algorithms only based on the sparsity assumption.The experimentalresults show the proposed method has better reconstruction performance than traditional methods.
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