Research on fault diagnosis of rolling bearing based on multi-sensor bi-layer information fusion under small samples

Abstract

To address the challenge of low fault diagnosis accuracy due to insufficient bearing fault data collected by single-sensor, a rolling bearing fault diagnosis method based on multi-sensor bi-layer information fusion under small samples is proposed. In the first-layer feature fusion, first, aiming at the problem that the number of intrinsic mode functions (IMFs) and the penalty factor in the variational mode decomposition (VMD) is challenging to determine, the Aquila optimizer algorithm is introduced to search for the optimal solution independently. Decomposition of bearing vibration signals acquired by multiple sensors using a parameter optimized the VMD method to obtain IMFs. The 12 time-domain features are then extracted for each IMF, and the maximum information coefficient (MIC) between each IMF time-domain feature and raw signal time-domain features is calculated. Finally, the feature fusion composition ratio is calculated according to the MIC mean of each. In the second layer of data fusion, the fusion composition ratio calculated in the first layer is used as a weight-to-weight and reconstructs the signals of each sensor to constitute a fused signal. Then, the fused signals are input into the fault diagnostic model, and fault pattern recognition and fault severity recognition are performed at the same time. The results show that the accuracy of the method proposed in this paper is higher than that of the comparison method on both the public dataset and the self-built experimental bench dataset, and it is an accurate, stable, and efficient fault diagnosis method.