Bayesian Compression and Reconstruction for Rotating Mechanical Vibration Signal Based on Laplace Prior and Sparse BlockCorrelation

doi:10.3969/j.issn.1000-1093.2021.12.024

Abstract

Abstract: For the difficult compression and reconstruction of mechanical vibration signal caused by high sampling frequency，an improved Bayesian compressive sensing algorithm is proposed by combining Laplace prior model with periodic sparse block of vibration signal for monitoring the equipment condition in real-time through wireless transmission. A Laplace distribution-based Bayesian priori model is proposed， which has stronger sparse promotion effect compared to Gaussian priori model. The vibration signal period is calculated according to the rotational speed and sampling frequency of mechanical equipment for dividing the signal periodically.The original signal expectation is estimated iteratively by fast correlation vector machine based on the feature of that multiple sparse blocks share the same hyperparameters.A two-stage parallel gearbox is selected as the research object. The compression and reconstruction simulations were carried out. It is found that the proposed method can effectively improve the reconstruction effect of mechanical vibration signals using the same sparse basis.

Key words: mechanicalvibrationsignal, Laplacepriori, sparseblock, Bayesiancompression, compressivesensing

CLC Number:

TH113.1

MA Yunfei， BAI Huajun， WEN Liang， GUO Chiming， JIA Xisheng. Bayesian Compression and Reconstruction for Rotating Mechanical Vibration Signal Based on Laplace Prior and Sparse BlockCorrelation[J]. Acta Armamentarii, 2021, 42(12): 2762-2770.

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