Submission Deadline: 31 December 2025 View: 44 Submit to Special Issue
Assist. Prof. Te Han
Email: hante@bit.edu.cn
Affiliation: Department of Management Engineering, Beijing Institute of Technology, Beijing, 100081, China
Research Interests: deep learning, transfer learning, renewable energy, prognosis and health management, large model
Dr. Rui Wu
Email: wurui18@mails.tsinghua.edu.cn
Affiliation: The Department of Energy and Power Engineering, Tsinghua University, Beijing, 100084, China
Research Interests: deep learning, self-supervised learning,prognosis and health management, large model
Dr. Jiachi Yao
Email: yaojc.2020@tsinghua.org.cn
Affiliation: School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China
Research Interests: intelligent maintenance of high-end equipment, smart energy management
Complex systems (e.g., aerospace, energy, manufacturing) play a key role in modern industries. By integrating real-time monitoring, fault diagnosis, lifetime prediction, and maintenance decision-making, prognostics and health management (PHM) serves as a critical technology to ensure the reliability, safety, and cost-effectiveness of the complex systems. However, the inherent characteristics of complex system datasparse, high-dimensional, multi-source, and cross-modalpose big challenges for traditional methods to produce robust results. Recent breakthroughs in Large Models (LMs), particularly Large Language Models (LLMs), have opened new research avenues for PHM. The sophisticated capabilities in reasoning, generalization, and multimodal data processing present significant potential for driving both academic research and practical applications in next-generation Prognostics and Health Management (PHM) technologies.
Main Topics:
· Intelligent Condition Monitoring and Fault Diagnosis with LMs
· Remaining Useful Life Prediction of Complex Systems with LMs
· Fine-tuning Techniques and Industrial Applications of LLMs
· Domain-Specific Large Models for Complex Systems
· Digital twin-based Predictive Analytics
· Multimodal Data Fusion for Industrial PHM
· Transfer Learning and Domain Adaptation in Industrial PHM
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