State Monitoring and Health Management of Complex Equipment (2nd Edition)

Dear Colleagues,

The development of complex industrial equipment combines mechanical, electronics, materials, and other interdisciplinary studies. The state monitoring and health management of complex equipment in aerospace, high-speed rail systems, and other industrial sectors are becoming increasingly complex. The difficulties of state monitoring and health management are not only due to the complexity of equipment but also to the integration of modeling techniques, mathematical algorithms, and maintenance policies. Therefore, the development of advanced state monitoring methods, prediction methods, and health assessment technology in industry would result in substantial benefits. This Special Issue is intended to collect state-of-the-art and future trends in state monitoring and health management methods in complex industrial equipment. Moreover, the potential objective of this Special Issue is to improve the reliability, safety, economy, and maintainability of complex equipment. Topics include papers on, but not limited to, reliability analysis, reliability optimization, failure prediction, signal processing and fault diagnosis, faults/state monitoring, remaining useful life estimation, health assessment, maintenance decision optimization, etc. This Special Issue welcomes papers on theoretical, analytical, technical, engineering, and experimental investigations of complex equipment. The contributions from this Special Issue will improve structural/system reliability analysis techniques, model-based and data-driven modeling methods, computer simulation technologies, reliability-based design optimization techniques, maintenance police optimization techniques, and other related interdisciplinary techniques in complex equipment reliability and health management.

Potential topics include, but are not limited to, the following:

• Structural/system state monitoring;
• Reliability evaluation and prediction;
• Reliability-based design optimization;
• Advanced signal processing, fault diagnosis, and fault monitoring methods;
• Model-based and data-driven detection for state monitoring and health management;
• Modeling and simulation methods for estimating the remaining useful life of complex systems or components;
• Health monitoring technologies;
• Machine learning and deep learning models for complex equipment health assessment;
• Maintenance and policy optimization for complex equipment;
• Performance estimation and prediction of complex equipment.

Prof. Dr. Cheng-Wei Fei
Guest Editor

Manuscript Submission Information

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