Mechanical Fault Diagnosis Under Variable Working Conditions Based on Sharpness AwarenessReinforced Convolutional Neural Network

Traditional deep transfer learning networks have effectively addressed the challenges arising from the asymmetry introduced by cross-domain data distributions in variable operational scenarios.It is achieved by leveraging knowledge learned from labeled fault data and applying it to the task of diagnosing unlabeled fault data collected under varying conditions.However,the inclusion of knowledge transfer modules has added complexity to the deep network's structure,resulting in a more intricate loss landscape.This,in turn,presents challenges for optimization.Traditional methods often struggle to navigate the sharpness of this loss landscape,leading to the model's parameters getting stuck in local minima characterized by high sharpness.This hinders model generalization and reduces accuracy.To tackle this challenge,this paper proposes the sharpness awareness reinforced con-volutional neural network(SA-CNN).This approach involves a joint optimization of the loss function and its flatness by assessing sharpness within a specified range.This process steers the fault diagnosis model parameters away from regions of high sharpness,ultimately improving model generalization.Extensive experiments on established mechanical fault diagnosis datasets demonstrate that,compared to traditional deep transfer learning-based fault diagnosis models,the proposed SA-CNN significantly enhances the performance of bearing fault diagnosis under varying working conditions.