Guest Editors
Prof. Ki Yong Oh
Email: kiyongoh@hanyang.ac.kr
Affiliation: School of Mechanical Engineering, Hanyang University, Seoul, Republic of Korea
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Research Interests: Prognostics and Health Management, Physcal AIs, AI Transformation, Battery Informatics
Prof. Hyunseok Oh
Email: hsoh@gist.ac.kr
Affiliation: Department of Mechanical and Robotics Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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Research Interests: Physics-informed machine learning, Industrial artificial intelligence, Fault diagnostics and prognostics, Design optimization
Prof. Daeil Kwon
Email: dikwon@skku.edu
Affiliation: Department of Industrial Engineering, Sungkyunkwan University, Suwon, Republic of Korea
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Research Interests: AI-PHM, Reliability and Risk Management, Electronics Packaging
Dr. Kyung Ho Sun
Email: sunkh@kimm.re.kr
Affiliation: Virtual Engineering Research Center, Korea Institute of Machinery and Materials, Daejeon, Republic of Korea
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Research Interests: Industrial AI, Computer Vision, Prognostics & Health Management, Rotational Machinery, Mechanical Vibration
Prof. Yun-ho Shin
Email: shinyh77@pusan.ac.kr
Affiliation: Department of Naval Architecture and Ocean Engineering, Pusan National University, Busan, Republic of Korea
Homepage:
Research Interests: Vision based condition monitoring, Autonomous system for naval ship, Naval ship survivability analysis
Summary
The rapid evolution of Artificial Intelligence (AI) has transformed Prognostics and Health Management (PHM), enabling smarter monitoring, prediction, and maintenance of complex systems and materials. AI-enabled PHM has become pivotal in ensuring greater reliability, safety, and cost effectiveness across industries, from manufacturing to structural systems and cyber-physical infrastructures.
This Special Issue aims to bring together cutting-edge research that advances the theory, methodologies, and applications of AI in PHM. We invite original contributions that deepen the understanding of AI-driven diagnostics, prognostics, and decision-making frameworks, bridging intelligent computation with practical health management challenges. Submissions should emphasize innovative algorithms, data-driven models, hybrid AI–physics approaches, and real-world case studies that enhance system resilience and lifespan.
Suggested themes include:
· Machine Learning and Deep Learning for Fault Detection and Prognostics
· AI-Based Predictive Maintenance in Cyber-Physical and Industrial Systems
· Digital Twins and Physics-Informed Models for Health Monitoring
· Sensor Fusion and Big Data Analytics for PHM
· Explainable and Trustworthy AI in Health Management
· Reinforcement Learning and Optimization for Health Decision Support
· Real-Time Monitoring and Edge-AI Solutions for PHM
Keywords
AI-enabled prognostics and health management, machine learning for PHM, predictive maintenance, digital twin, physics-informed artificial intelligence, fault diagnosis and prognostics, sensor fusion and condition monitoring, explainable AI for reliability, cyber-physical systems health monitoring
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