Topic: Artificial Intelligence-Based Fault Diagnosis and Intelligent Operation and Maintenance of Critical Equipment Components

The reliable operation of industrial equipment is the backbone of modern industrial systems across sectors, including manufacturing, energy, aerospace, and transportation. Failures in critical components—such as bearings, gears, and rotors—can lead to catastrophic breakdowns, significant economic losses, and serious safety hazards. Recently, artificial intelligence (AI) has revolutionized traditional condition monitoring by providing powerful data-driven frameworks capable of extracting complex fault features from massive, noisy industrial data. By integrating advanced AI algorithms with mechanical dynamics, highly accurate fault diagnosis, predictive maintenance, and intelligent decision-making can be achieved. This Special Issue seeks high-quality contributions that advance the theory, algorithms, system design, and real-world applications of AI-driven fault diagnosis and intelligent operation and maintenance (O&M) for industrial equipment.

Topics of interest include, but are not limited to:

● Deep learning and machine learning methods for fault diagnosis of critical mechanical components;

● Predictive maintenance strategies and remaining useful life (RUL) prediction using AI-based frameworks;

● Intelligent condition monitoring and anomaly detection for industrial equipment;

● Transfer learning and domain adaptation for fault diagnosis under varying operating conditions;

● Physics-informed neural networks (PINNs) and digital twin technologies for intelligent O&M;

● Multimodal and multi-sensor data fusion approaches for mechanical health assessment;

● Explainable artificial intelligence for transparent and reliable fault diagnosis;

● Edge computing and real-time AI deployment in industrial machinery;

● AI-enhanced signal processing techniques for weak fault feature extraction;

● Industrial case studies of intelligent O&M in robotics and complex mechanical systems.

Equipment, fault diagnosis, intelligent operation and maintenance, artificial intelligence, predictive maintenance, deep learning, condition monitoring, remaining useful life (RUL), digital twin, explainable artificial intelligence