Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects

Area of Science:

• Machine Learning
• Artificial Intelligence
• Industrial Automation

Background:

• Deep Transfer Learning (DTL) integrates deep learning and transfer learning for advanced feature representation and knowledge transference.
• DTL shows promise in Intelligent Fault Diagnosis (IFD) but faces challenges in complex domains.
• Adversarial Deep Transfer Learning (ADTL) has emerged to address these limitations.

Purpose of the Study:

• To review and categorize Adversarial Deep Transfer Learning (ADTL) models.
• To examine recent advancements of ADTL in the Intelligent Fault Diagnosis (IFD) field.
• To identify current challenges and future directions for ADTL in fault diagnosis.

Main Methods:

• Categorization of ADTL into non-generative and generative models.
• Review of ADTL advancements focusing on feature transference, mapping relationships, and feature transformation using Generative Adversarial Networks (GANs).
• Analysis of ADTL applications in Intelligent Fault Diagnosis (IFD).

Main Results:

• Non-generative ADTL models focus on efficient feature transference and mapping.
• Generative ADTL models utilize techniques like GANs for feature transformation.
• Recent advancements show ADTL's potential in improving IFD.

Conclusions:

• ADTL offers significant potential for Intelligent Fault Diagnosis (IFD).
• Key challenges include data imbalance, negative transfer, and adversarial training stability.
• Future research should focus on optimizing ADTL for real-world industrial fault diagnosis.