Domain Adversarial Transfer Learning Bearing Fault Diagnosis Model Incorporating Structural Adjustment Modules

With the improvement in industrial equipment intelligence and reliability requirements, bearing fault diagnosis has become a key technology to ensure the stable operation of mechanical equipment. Traditional bearing fault diagnosis methods are ineffective in diagnosing complex faults and mostly rely on the manual adjustment of hyperparameters. To this end, this paper proposes a domain adversarial migratory learning bearing fault diagnosis model incorporating structural adjustment modules. First, the pre-trained model of the source domain is applied to the target domain dataset through an adversarial domain adaptation technique. Then, the network depth and width are dynamically adjusted in the Optuna optimization framework to accommodate more complex fault types in the target domain. Finally, the performance of the model is further improved by automatically optimizing the hyperparameters. The experimental results show that the model exhibits high accuracy in the diagnosis of different fault types, especially in the face of complex and variable industrial environments, demonstrating strong adaptability and robustness. The method provides an effective solution for fault diagnosis of intelligent devices.