Disentangle-and-aggregate feature learning (DAFNet) for motor bearing fault diagnosis

Abstract

Abstract To address the issues of parameter redundancy and low computational efficiency in traditional convolutional neural networks (CNNs) for motor bearing fault diagnosis, which are caused by increasing network depth, this paper proposes a Disentangle-and-Aggregate Feature Learning Network (DAFNet). This method is designed to overcome the challenges of model deployment on resource-constrained edge devices. Moving beyond the conventional strategy of simply stacking layers, DAFNet innovatively adopts a hierarchical disentanglement and aggregation mechanism. It utilizes a secondary splitting strategy to disentangle shallow, medium, and deep features, followed by terminal feature fusion to achieve an effective representation of fault information. Experimental results based on the CWRU dataset demonstrate that DAFNet achieves a 100% average accuracy in fault diagnosis while significantly reducing both computational overhead and parameter count. Compared with existing mainstream lightweight models, this method exhibits superior generalization performance and inference speed, providing new theoretical support for the efficient application of deep learning in industrial embedded systems.