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

Area of Science:

• Mechanical Engineering
• Artificial Intelligence
• Machine Learning

Background:

• Bearing fault diagnosis is crucial for mechanical equipment reliability.
• Traditional methods struggle with complex faults and manual hyperparameter tuning.

Purpose of the Study:

• To develop an intelligent bearing fault diagnosis model for industrial equipment.
• To address limitations of traditional methods in complex fault diagnosis and hyperparameter optimization.

Main Methods:

• A domain adversarial migratory learning model with structural adjustment modules was proposed.
• Adversarial domain adaptation transferred a pre-trained model to a target dataset.
• The Optuna optimization framework dynamically adjusted network architecture and hyperparameters.

Main Results:

• The model achieved high accuracy in diagnosing various bearing fault types.
• Demonstrated strong adaptability and robustness in complex industrial environments.
• Successfully addressed challenges posed by complex and variable operating conditions.

Conclusions:

• The proposed model offers an effective solution for intelligent device fault diagnosis.
• Highlights the potential of migratory learning and automated optimization for industrial applications.
• Enhances the stability and reliability of mechanical equipment through accurate fault detection.