Fault diagnosis of rolling bearings based on dynamic convolution and dual-channel feature fusion under variable working conditions

Abstract

Addressing the challenge of inconsistent data feature distribution and the difficulty of fault diagnosis in rolling bearings operating under variable conditions, a novel approach is proposed for bearings fault diagnosis. Dynamic convolution and dual-channel feature fusion are utilized in our method. In the shallow network layer, we employ a dual-channel convolutional structure, combining a large convolutional group with a small convolutional group to enhance the extraction of high-low frequency fault information from images. The improved GhostNetV2 bottleneck layer was used in the deeper layer of the network to obtain more beneficial features through the dynamic convolution and attention mechanism. Finally, fault classification and evaluation under variable working conditions was performed on the Case Western Reserve University and Drivetrain Dynamic Simulator (DDS) datasets. Our results showed that the methods and model used in this study can effectively handle the precision fault detection across various operational scenarios.