From Corrective and Preventive Maintenance to Prognostics

The modern industry faces the challenge of prolonging the lifespan of high-performance systems while maintaining their operational efficiency and cost-effectiveness. Through well-planned maintenance strategies, industries can achieve significant reductions in life cycle costs while minimising operational downtime and improving system reliability and its availability. Prognostics and Health Management (PHM) is an integrative, system-level engineering framework that combines diagnostics, prognostics, and decision-support processes to enable informed asset health management throughout the system lifecycle. While diagnostics and prognostics are core components of condition-based maintenance (CBM) and predictive maintenance (PdM), PHM emphasises their systematic integration within a closed-loop process that links operational activities with organizational and life cycle considerations. Thus, PHM does not replace existing maintenance policies; rather, it provides a unified framework that aligns health information, prognostic outputs, and maintenance decisions with resource constraints, risk, and performance objectives. Despite rapid advances in algorithmic research, a significant gap exists in the foundational conceptual understanding required by practitioners and researchers who are new to the field. During a literature review of machine and deep learning applications in the field of Predictive Maintenance (PdM) and estimation of the Remaining Useful Life (RUL), the authors observed that while extensive attention is given to algorithmic developments and application-specific studies, a consolidated perspective on PHM's fundamental role, historical evolution, and strategic implementation of PHM remains notably absent. This perspective article addresses this gap by providing a clear conceptual framework and practical roadmap of maintenance models, ranging from corrective and preventive to condition-based and predictive approaches, rather than introducing new models. This study articulates PHM’s core functional elements of PHM and positions it as an integrative, system-level paradigm that contextualises established maintenance strategies, such as CBM and PdM. In addition, it discusses the technological and industrial drivers that led to the emergence of PHM, providing an accessible entry point for researchers and practitioners.