Advances in Intelligent Fault Diagnosis for Complex Industrial Equipment

Dear Colleagues,

The relentless pursuit of industrial automation has led to the widespread deployment of complex industrial equipment, ranging from wind turbines and aero-engines to high-speed trains and large-scale manufacturing systems, whose reliable operation is the bedrock of modern economic productivity and safety. Any unexpected failure can trigger a cascade of costly consequences, including unplanned downtime, extensive economic losses, and potentially catastrophic safety hazards. This critical dependency has propelled the field of fault diagnosis to the forefront of industrial research.

Conventional fault diagnosis methods, primarily rooted in manual feature extraction and linear signal analysis, are increasingly inadequate. They struggle to decipher the non-linear, non-stationary signatures and high-dimensional data characteristic of modern industrial processes, creating a significant gap between diagnostic capability and practical need. In response, the field is experiencing a paradigm shift driven by Artificial Intelligence. "Advances in Intelligent Fault Diagnosis for Complex Industrial Equipment" harnesses the power of deep learning, transfer learning, and other data-driven techniques to learn fault patterns autonomously. This evolution from reactive maintenance to proactive health management is crucial: it not only enhances equipment reliability and operational safety but also reduces maintenance costs. This Special Issue aims to showcase cutting-edge research that bridges the gap between AI and industrial reliability, shaping the future of intelligent equipment health management. The scope of this Special Issue, titled “Advances in Intelligent Fault Diagnosis for Complex Industrial Equipment”, encompasses a broad range of research areas, including, but not limited to, the following:

(1) Intelligent detection and monitoring, including incipient fault detection, real-time anomaly detection and multi-modal sensor fusion for equipment monitoring;

(2) Intelligent diagnostics and prognostics, including fault identification, severity assessment, root cause analysis and remaining useful life prediction based on deep learning, federated learning, and hybrid models;

(3) Lifecycle management and adaptation, including domain adaptation, transfer learning across different equipment and continuous learning strategies to ensure diagnostic models remain accurate throughout the equipment lifecycle;

(4) Prognostics and health management systems, including system architecture design, cloud-edge collaboration, digital twins for health management, and decision support systems for maintenance optimization;

(5) Industrial applications, highlighting real-world applications of intelligent fault diagnosis across diverse sectors, including aerospace, renewable energy systems, precision machinery, manufacturing systems and others.

We look forward to receiving your contributions.

Dr. Jingjing Gao
Prof. Dr. Xu Yang
Dr. Jiaorao Wang
Dr. Dajian Huang
Guest Editors

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• fault diagnosis
• lifecycle management
• prognostics and health management
• artificial intelligence
• deep learning
• federated learning
• transfer learning
• digital twin
• domain adaptation