Transfer Learning Methods in Equipment Reliability Management

Dear Colleagues,

Equipment reliability management is a critical issue in the engineering field, particularly concerning how to ensure that equipment maintains good performance and reliability throughout its entire life cycle. In recent years, with the rapid development of industrial automation and smart manufacturing, collection and analysis of equipment data have become increasingly important. In this context, transfer learning, as an effective machine learning method, has shown great application potential in equipment reliability management. Transfer learning can utilize the knowledge learned from related domains or tasks to help solve problems such as data scarcity and limited computational resources in the target domain, thereby improving the performance of key tasks such as equipment fault diagnosis and predictive maintenance.

Although transfer learning has shown great potential in equipment reliability management, this field still faces some key challenges. For example, due to the differences in structure and working environment between different forms of equipment, how to ensure that the transferred knowledge is relevant and applicable to the target task is a topic that requires in-depth research. Secondly, how to effectively extract and transfer valuable knowledge from source tasks is an urgent problem to be solved. Furthermore, in the specific field of equipment reliability management, how to design efficient transfer learning algorithms and frameworks to fully utilize the advantages of transfer learning is also a worthy research direction. Finally, how to fully integrate transfer learning strategies with reliability analysis techniques such as fault diagnosis and life prediction to achieve more intelligent and comprehensive decision support, while ensuring the generality and practicality of the methods, still requires extensive framework research and application verification.

This Special Issue aims to provide a comprehensive platform for researchers, engineers, and industry practitioners to share their methods or experiences in applying transfer learning to solve equipment reliability problems. We hope that this Special Issue can generate concentrated and consistent contributions in the following areas, including, but not limited to, the following:

• Domain adaptation technology;
• Domain generalization technology;
• Few-shot fault diagnosis and prediction methods;
• Zero-shot fault diagnosis and prediction methods;
• Interpretability model;
• Federated learning and transfer learning;
• Meta-learning and transfer learning;
• Other research about transfer learning and equipment reliability managemen

Dr. Lei Wang
Dr. Xin Zhang
Dr. Xixing Li
Guest Editors

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• equipment reliability
• transfer learning
• domain adaptation
• domain generalization
• interpretability
• fault diagnosis
• fault prediction