MLDA-GLFD: A multilayer domain-adaptive rolling bearing fault diagnosis method fusing global and local features

In the field of fault diagnosis, transfer learning methods have achieved remarkable progress in rolling bearing fault diagnosis. However, existing approaches still face challenges in feature extraction and feature alignment between source and target domains, which remains difficult for feature networks to effectively capture both local and global features, and the distribution discrepancies between the two domains further degrade the performance of transfer models. To address these issues, this paper proposes a multilayer domain-adaptive fault diagnosis method that integrates global and local feature representations (MLDA-GLFD). Specifically, an enhanced local feature extraction module (SLFE) is designed by combining depthwise separable convolution and partial convolution to capture local feature information more precisely. In addition, a global feature extraction module (GFAB) is constructed, which incorporates a multi-head self-attention mechanism (MHSA), a global context block (GCblock) and a pyramid pooling module (PPM) to jointly strengthen global feature extraction. To further achieve feature distribution alignment between the source and target domains, a dynamic convolution (DConv) module with a hierarchical domain alignment mechanism is designed to adaptively adjust the receptive field of convolutional kernels. Moreover, a combination of Maximum Mean Discrepancy (MMD) and Multiple Kernel MMD (MKMMD) is employed to accurately align inter-domain features, thereby enhancing the model’s transferability to the target domain. Experimental results on two rolling bearing datasets, CWRU and SDUST, demonstrate that the proposed MLDA-GLFD method achieves average accuracies of 96% and 94%, respectively, significantly outperforming other comparative methods.