AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems

Dear Colleagues,

The safe, efficient, and intelligent operation of complex industrial systems is essential to the sustainable development of key industries such as energy and power, rail transportation, aerospace, process manufacturing, and intelligent equipment. These systems often exhibit strong coupling, time-varying operating conditions, and diverse failure modes. Traditional scheduled or experience-based maintenance strategies are increasingly insufficient to meet modern demands for high reliability, reduced costs, and full life-cycle management.

Recent advances in sensing, data acquisition, and computing have accelerated the adoption of data-driven prognostics and health management (PHM) methods. In particular, integrating artificial intelligence (AI), edge computing, digital twins, and foundation models has unlocked new capabilities in early fault detection, remaining useful life (RUL) prediction, and adaptive maintenance optimization. Furthermore, the emergence of explainable AI techniques has enhanced the transparency and trustworthiness of intelligent maintenance systems.

This Special Issue will gather high-quality original research and reviews on the latest innovations, methodologies, and applications in AI-enabled PHM for complex industrial systems. Topics include, but are not limited to, the following:

• Multi-source heterogeneous data fusion;
• Anomaly detection, fault diagnosis, and RUL prediction;
• AI–digital twin integration for intelligent health management;
• Hybrid modeling combining physics-based and data-driven methods;
• Explainable AI and foundation models for industrial monitoring;
• Applications across energy, transportation, aerospace, and manufacturing.

Dr. Dandan Peng
Dr. Xiaoxi Hu
Dr. Jipu Li
Prof. Dr. Chuanjiang Li
Guest Editors

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