As an emerging field in the mechanical sciences, prognostics and health management (PHM) are gaining interest from the industry and academia. An effective PHM framework normally includes health prognostics and maintenance management. For health prognostics, more and more advanced instruments, such as smart sensors, meters, controllers, and computational devices, are being applied to collect and analyse the signals from individual machines. Prognostic techniques, such as vibration monitoring, oil analysis, temperature detection, acoustic emission, and ultrasonic inspection, have also been widely employed to measure the status of a machine. Many valuable prognostics approaches have thus been proposed to generate a rational estimation of the remaining useful life (RUL) or the potential degradation process. For maintenance management, the maintenance policies of complex systems are facing challenges from structural, stochastic, and economic dependencies, and advanced manufacturing paradigms.

Due to recent developments in manufacturing paradigms, PHM methodologies for traditional manufacturing systems need to be extended. There has been increasing interest in integrating PHM with intelligent manufacturing. With innovations in technique, enterprises can apply mass customisation, reconfigurable manufacturing, sustainable manufacturing, and service-oriented manufacturing to maintain competitiveness and meet customer needs. More targeted PHM methodologies enable the industry to lower the possibility of unexpected breakdowns and to lower the cost of maintenance. Thus, novel PHM methodologies for intelligent manufacturing are vital for enterprises with foresight.

The aim of this Special Issue is to promote prognostics and health management, and act as a platform to present high-quality original research on the latest developments of PHM methods for intelligent manufacturing. We welcome both original research articles and review articles discussing the current state of the art.

Potential topics include but are not limited to the following:

• Model-driven approaches in PHM
• Data-driven approaches in PHM
• Machine learning based system health monitoring
• Failure detection, classification, or localisation
• Advanced signal conditioning techniques
• Optimisation of intelligent sensing layout
• Safety, reliability, risk, and life-cycle performance
• Maintenance policies for intelligent manufacturing
• PHM applications of advanced manufacturing paradigms
• Operational and experimental modal analysis
• Risk, reliability, and uncertainty in PHM