Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives.

Area of Science:

• Automation Systems Engineering
• Machine Learning
• Artificial Intelligence

Background:

• Modern automation systems require robust fault diagnosis (FD) for safety and reliability.
• Challenges like environmental variations and component degradation necessitate adaptive FD methods.
• Transfer learning offers a paradigm for developing FD approaches with long-term applicability.

Purpose of the Study:

• To provide a comprehensive review of transfer learning-motivated fault diagnosis methods.
• To focus on the specific application of transfer learning in FD tasks.
• To present novel principles and a classification strategy for these techniques.

Main Methods:

• Systematic review of existing literature on transfer learning for fault diagnosis.
• Categorization of methods into two subclasses: knowledge calibration and knowledge compromise.
• Analysis of how prior knowledge is utilized for FD tasks.

Main Results:

• Identification of transfer learning as a key enabler for self-learning and adaptive FD.
• Presentation of three guiding principles for applying transfer learning in FD.
• Introduction of a new classification strategy for transfer learning-motivated FD techniques.

Conclusions:

• Transfer learning is crucial for developing resilient and adaptive fault diagnosis systems.
• The presented review and classification offer a structured understanding of the field.
• Open problems and future research directions in transfer learning for FD are highlighted.