Bearing fault diagnosis based on multi-branch enhanced GhostNet with adaptive focal loss

Insufficient fault samples and severe class imbalance present significant challenges for intelligent bearing fault diagnosis. To overcome these issues, this paper proposes a lightweight diagnostic approach that combines an adaptive focal loss function with a multi-branch enhanced ghost network, aiming to improve both accuracy and efficiency under limited and imbalanced data conditions. Specifically, for the small-sample problem, a Lightweight Multi-Branch Enhanced Network (LMBE) is constructed, centered on a novel Multi-Branch Enhanced Ghost Bottleneck (MBEG-bneck) designed to capture high-frequency features and enhance fault pattern recognition. For the class-imbalance problem, a Variable Focusing Class-Balanced Focal Loss (VF-CBFL) is established, which uses a tangent-based dynamic focusing mechanism to adaptively adjust the model's emphasis on hard and easy samples during training, while incorporating a class-balance factor to handle differences in sample numbers across classes. By jointly enhancing feature extraction and loss optimization, the proposed method effectively alleviates the limitations caused by small samples and data imbalance. Experiments conducted on two bearing datasets demonstrate that the proposed method achieves high diagnostic accuracy and strong generalization capability, attaining an accuracy of 98.96% under the extreme imbalance scenario of 10:1 with limited samples.