Feature-aware dynamic expert network for long-tailed rolling bearing fault diagnosis

The intelligent fault diagnosis of rolling bearing is crucial for ensuring stable operation of rotating machinery. However, in industrial settings, the normal data far exceeds fault data, resulting in a long-tailed distribution. This issue causes existing intelligent diagnosis models to be biased toward the normal class, making it difficult to identify tail-class faults. To address this issue, a novel network is proposed named Feature-aware Dynamic Expert (FADE) for rolling bearing fault diagnosis under long-tailed data. Within this network, a feature-aware balanced sampler is constructed, which uses sample feature distinctness to quantify learning value and assigns high sampling weights to hard-to-classify tail-class samples. In proposed FADE network, a dynamic routing-based multi-expert fusion module is proposed, which utilizes a routing mechanism to assign expert weights at the sample level, enabling different experts to focus on specific fault patterns. A multi-objective optimization constrained loss is designed to dynamically adjust the learning pace for head and tail classes. Through the coordinated constraints of multiple losses, it achieves balanced learning for both head and tail classes and enables precise capture of fault features. Experimental results on the public dataset and laboratory dataset show that the proposed method achieves high fault diagnosis accuracies. Compared to other mainstream methods, the proposed FADE model demonstrates better overall performance in the task of intelligent fault diagnosis for bearings.