Condition monitoring is one of the research priorities for equipment reliability and health management, through which the health integrity of the machines can be guaranteed. With the development of intelligent manufacturing and industry 4.0 technologies, data-driven modeling and analysis technologies (such as big data technologies, artificial intelligence, etc.) have received extensive attention. Fault detection, fault diagnosis, and remaining useful life prediction have become hotspots in this research area.

In recent decades, numerous signal processing and dynamic techniques have been developed for health management of equipment. However, high-level knowledge of the machine and failure mechanisms is required to implement the above-mentioned techniques. Thus, it is necessary to develop artificial intelligence methodologies to monitor the health status of equipment.

This Special Issue welcomes original research and review articles on the condition monitoring of industrial equipment based on artificial intelligence technology. We invite scientists and researchers to provide contributions which solve the main problems faced in the field.

Potential topics include but are not limited to the following:

• Theoretical research and numerical experiments of industrial equipment for data-driven process modelling
• Fault diagnosis technology of industrial equipment based on artificial intelligence algorithm
• Remaining useful life prediction technology of industrial equipment
• Reliability analysis of industrial equipment
• Online health management verification technology for industrial equipment
• Algorithm and implementation of industrial equipment prediction, diagnosis, and health management
• Fault analysis and prevention technology of industrial equipment
• Analysis and processing technology of state information of industrial equipment
• Analysis on the development prospect of industrial equipment fault diagnosis and early warning technology
• Deep learning in the area of condition monitoring of industrial equipment