Sim-ConvFormer: A lightweight troubleshooting framework using SimAM and External Attention

In recent years, traditional convolutional neural networks (CNN) and Transformer models have been widely used in industrial fault diagnosis. This paper proposes a new lightweight fault diagnosis framework, Sim-ConvFormer, to address the issues of high model complexity and stringent hardware requirements. The Sim-ConvFormer framework integrates SimAM and External Attention. SimAM enhances the model's sensitivity to detailed signal changes by capturing locally significant features at different scales. External Attention, contrasting with self-attention, improves generalization by calculating the affinity between input features and two external learning memories shared by the entire dataset, thereby capturing global context information. The synergy of these technologies not only preserves their individual advantages but also enhances the model's robustness and accuracy in handling various faults. Experiments on three different mechanical systems demonstrate Sim-ConvFormer's superior fault diagnosis performance, particularly in terms of model lightness and diagnostic robustness compared to existing Transformer and CNN-based methods. These results indicate that Sim-ConvFormer is an effective fault diagnosis tool suitable for deployment in resource-constrained industrial environments.