High-precision bearing signal recovery based on signal fusion and variable stepsize forward-backward pursuit

Abstract

Abstract In multi-sensor, long-distance fault monitoring of rolling bearings, the bearing signals are compressively sampled, transmitted, and reconstructed according to the theory of compressive sensing. However, the reconstruction accuracy and speed are limited and are affected by the noise afflicting the collected signals. In this paper, a high-precision signal recovery method, based on signal fusion and the variable stepsize forward–backward pursuit (VSFBP) algorithm, is proposed. First, the method adaptively adjusts the best estimate of the traditional random weighted fusion algorithm, by using the relative fluctuation value, which can fuse variable signals and reduce the noise component of the detection signal. Second, two fuzzy parameters are used to control the step sizes of the atom selection and deletion in the two-stage matching pursuit algorithm; this improves the reconstruction accuracy and speed of the algorithm under a high compression ratio. Finally, to prevent excessive backtracking, in the two-stage matching pursuit algorithm, the observation matrix is updated after each iteration, which improves the reconstruction accuracy of the algorithm further. Simulation and experimental results are compared to verify the effectiveness of the proposed method.