Simulation data-driven adaptive frequency filtering focal network for rolling bearing fault diagnosis

The scarcity of well-labeled samples severely limits the application of deep learning-based fault diagnosis methods. To address this issue, this paper proposes a novel domain-adaptive intelligent diagnostic method, termed a simulation data-driven adaptive frequency filtering focal network, which transfers knowledge from dynamic simulation data to unlabeled measured data. First, a dynamic simulation model is established to simulate the coupled behavior of rolling bearings and generate substantial labeled simulation data. Subsequently, an adaptive frequency filter is introduced to suppress random interference components in the frequency domain, complementing conventional time-domain feature extraction methods, thereby reducing the distribution discrepancy between simulation and measured data. Finally, a domain-aware weighted focusing strategy is proposed to adjust sample weights based on predicted domain labels, helping the model focus on difficult samples with significant distribution discrepancies and reduce misclassification. Experimental results demonstrate that the proposed method effectively integrates simulation and measured data, achieving high-accuracy fault diagnosis of rolling bearings in unlabeled measured data. The proposed approach offers a promising fault diagnosis solution in scenarios where obtaining labeled samples is challenging or impractical.