Multi-sensor signal fusion method for rolling bearing based on the standard relative mean–variance value and random weighting algorithm

Abstract

In the study, an optimization fusion method based on the standard relative mean–variance value and a random weighting algorithm is proposed to effectively utilize multisensory-acquired signals for realizing accurate monitoring of rolling bearing faults. First, a novel feature-standard relative mean variance value is developed to adjust the balance parameter in the random weighting algorithm to adaptively obtain the best estimate value. Second, upper and lower thresholds of the balance parameter are provided to improve the noise reduction and feature retention abilities of the algorithm in the fusion process. Finally, the weight of each sensor signal is determined based on the overall variance of each sensor. The parameters in the optimization fusion method can be adaptively adjusted based on the collected signal, which has practical value. Simulations and laboratory experiments demonstrate the effectiveness of the method.