A novel frequency attention mechanism for FEM-assisted bearing fault diagnosis

Class imbalance remains a persistent challenge for intelligent fault diagnosis (IFD) algorithms in practical applications. Expanding imbalanced data sets using finite element model (FEM) data offers a promising solution. However, significant distributional discrepancies between FEM data and actual measured signals pose challenges for effective information fusion. In this study, we propose a novel FEM-assisted fault diagnosis paradigm to address the class imbalance issue by combining domain adversarial (DA) strategies with an adaptive fault frequency attention module (AFFAM). First, we apply the variational mode decomposition algorithm to partition the frequency spectrum, facilitating model optimization through the incorporation of prior knowledge, thus laying a foundation for subsequent multi-dimensional feature extraction. We then employ the proposed AFFAM to extract multi-dimensional features from the input, enabling the model to integrate information from different granularity levels and bridging the information gap between simulated and real-world data. Finally, data integration is achieved through a data analysis strategy. The DA-AFFAM framework is validated using a customized bearing test bench. Results indicate that the classification accuracy for multi-class imbalance problems reaches 92.88%, even with a class imbalance ratio of 200:1.