Intelligent Fault Diagnosis and Predictive Maintenance for Machinery: Advanced Signal Processing and AI-Driven Approaches

Dear Colleagues,

Alongside the increasing dissemination of Industry 4.0 and intelligent manufacturing, the health management of machinery and equipment is undergoing a paradigm shift from "sensing" to "cognition", and from "diagnosis" to "prediction." Traditional maintenance methods based on rules and fixed thresholds are no longer sufficient to cope with the challenges of complex operating conditions, new failure modes, and massive amounts of multi-source data. Cutting-edge technologies such as artificial intelligence, digital twins, and edge computing provide the core impetus for building a new generation of intelligent operation and maintenance systems that are self-sensing, self-decision-making, and self-evolving. This Special Issue aims to gather the latest breakthrough research from scholars worldwide in the field of intelligent fault diagnosis and predictive maintenance, focusing on algorithmic innovation, cross-domain integration, and engineering implementation, promoting the development of this field towards higher precision, stronger generalization, and greater interpretability, and providing a solid technological foundation for industrial intelligence. Some potential topics for exploration include:

• Fault evolution simulation and life prediction based on digital twins;
• Innovative applications of deep learning and large models in machinery health management;
• Edge intelligence and lightweight diagnostic algorithms;
• Fusion of explainable artificial intelligence and fault physical mechanisms;
• Multimodal fusion and cross-domain perceptual intelligence;
• Small sample, zero sample, and open set fault diagnosis;
• Intelligent operation and maintenance systems and closed-loop decision making;
• Advanced signal processing and adaptive feature engineering.

Dr. Chaoyong Ma
Dr. Kun Zhang
Dr. Yanping Zhu
Guest Editors

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