Fault diagnosis method of drum bearing based on multi-sensor data fusion under small samples

Abstract With the increasing complexity of industrial equipment, the fault diagnosis of rolling bearings, which are important rotating mechanical components, is particularly critical. Rolling bearing fault diagnosis under small-sample conditions faces challenges of data scarcity and insufficient model validity. In this paper, a fault diagnosis method based on multi-sensor data fusion is proposed to solve the problem of rolling bearing fault diagnosis under small-sample conditions. First, the multi-sensor time–frequency image fusion module is designed to convert the multi-source 1D vibration signals into multi-source 2D fused images by short-time Fourier transform, preserving the time–frequency characteristics of the original signals. Second, the expansion residual shrinkage weighting module is constructed to extract multi-scale features from multi-source information by using multi-scale expansion convolution and improved residual shrinkage module, while the global information selection mechanism is designed to allow the network to focus on key features. Finally, a pyramid feature fusion network is constructed to minimize the information loss phenomenon, and the identification of different states is performed using Softmax classifiers. Experimental validation was performed using the Case Western Reserve University bearing dataset bearing dataset and Southeast University bearing dataset bearing dataset, and the results show that the proposed method has excellent fault diagnosis and good generalization ability under small-sample conditions.