Next-Generation Fault Detection and Diagnosis: AI-Driven Models, Predictive Maintenance, and Adaptive Systems

Dear Colleagues,

The rapid evolution of industrial systems, energy infrastructures, and complex engineering assets has brought new challenges to the early detection, diagnosis, and prevention of faults. Traditional fault detection methods, while valuable, are often limited in their ability to handle large-scale, heterogeneous, and dynamically changing systems. The rapid progress of artificial intelligence (AI) has profoundly reshaped the field of fault detection and diagnosis in complex engineering systems. AI-driven diagnostic methods offer unprecedented opportunities for developing predictive maintenance strategies and adaptive diagnostic frameworks.

This Special Issue aims to collect high-quality and original research contributions on innovative theories, methods, and applications in next-generation fault detection and diagnosis. Contributions may address, but are not limited to, the following topics:

• AI-driven models for fault detection, diagnosis, and prognosis;
• Predictive maintenance strategies;
• Digital twins for condition monitoring and diagnostics;
• Adaptive and self-learning diagnostic systems;
• Big data analytics and uncertainty modeling in industrial systems;
• Explainable AI for diagnostics;
• Multi-sensor fusion and signal processing for fault detection;
• Applications to energy systems, manufacturing, transportation, robotics, and other safety-critical domains.

Dr. Chuancang Ding
Dr. Jinyang Jiao
Dr. Baoxiang Wang
Guest Editors

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