Intelligent Maintenance and Health Management of Electromechanical Equipment

Dear Colleagues,

Electromechanical equipment has been extensively used in various industrial applications, such as aerospace, the petrochemical industry, metallurgy, power generation, and various military systems. However, the complex and harsh working environment made the electromechanical equipment prone to failure. Therefore, the maintenance and health management of electromechanical equipment and its key components are essential to ensure their safe and stable operation. Intelligent maintenance and health management aim to combine intelligent tools and key techniques, such as data quality assurance, condition monitoring, fault diagnosis, degradation assessment, and useful life prediction and maintenance decisions, to help avoid unexpected economic loss and even serious accidents caused by the sudden shutdown of electromechanical equipment, so as to realize the continuous advancement of smart manufacturing and the continuous transformation of the industry. Therefore, intelligent maintenance and health management can benefit industrial production and significantly improve productivity and automation.

This Special Issue focuses on advanced algorithms/techniques for the intelligent maintenance and health management of electromechanical equipment.

Potential topics include but are not limited to:

• Intelligent maintenance and health management based on digital twin;
• Intelligent maintenance and health management based on signal processing;
• Intelligent maintenance and health management based on machine learning;
• Intelligent maintenance and health management based on deep learning;
• Intelligent maintenance and health management under non-stationary conditions;
• Intelligent maintenance and health management based on multi-source information fusion;
• Wear and fatigue analysis.

Dr. Ke Feng
Dr. Zihao Lei
Dr. Yadong Xu
Dr. Zhijun Ren
Dr. Qing Ni
Prof. Dr. Guangrui Wen
Guest Editors

Manuscript Submission Information

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