Health monitoring, diagnostic, prognostic systems are becoming more urgent need in many engineering domains such as mechanical engineering, electrical engineering, civil engineering, and biomedical engineering. Intelligent health monitoring systems have a capability to acquire and apply knowledge in an intelligent manner and have the capabilities of perception, reasoning, learning, and making decisions from incomplete information. Therefore, intelligent system approaches for monitoring, diagnosis, and prognosis can pave a practical way for a variety of engineering applications in the absence of human interaction.

The aim of this Special Issue is to bring together original research and review articles discussing intelligent monitoring, diagnosis and prognosis for electromechanical equipment. We welcome submissions including theories aimed at further understanding intelligent techniques, including neurocomputing, deep learning, fuzzy logic, evolutionary algorithms and swarm intelligence. Moreover, this Special Issue also welcomes manuscripts including innovative engineering applications focused on health monitoring, diagnosis, and prognosis of physical processes or systems with no (or very little) human interaction. The aim of this special issue is to create an international forum for scientists and practicing engineers to publish the latest research findings and ideas.

Potential topics include but are not limited to the following:

• Structural health monitoring and damage assessment with electromechanical equipment
• Condition monitoring and non-destructive evaluation with electromechanical equipment
• Image-based and vibro-acoustic-based diagnosis with electromechanical equipment
• Prognosis of remaining useful life of core equipment in mechanical engineering
• Internet of Things-based health monitoring of electromechanical equipment
• Fault feature processing and weak signal enhancement
• Vibration control and fault-tolerant control for fault isolation
• Defect severity estimation using degradation estimation methods
• Machine learning methods and hybrid algorithms in industrial applications
• Multiple or multi-type sensor fusion and feature selection