1 Chapter 1 Introduction 1.1 Research context and motivation Modern societies rely on a variety of engineering systems that help maintain order, ensure safety, provide comfort, as well as promote growth and well-being. Engineering systems are specifically designed, built, and managed to address challenges by combining di!erent technological parts. They cover many areas such as mechanical, electrical, civil, and computer engineering, and are crucial in building and maintaining our infrastructure, producing goods, managing transportation, and handling information technology. These systems are usually complex and have many interconnected components that depend on each other. Careful management and continuous improvement are key for them to work as intended. According to fundamental principles of physics, everything naturally progresses towards a state of wear and breakdown through deterioration (McPherson, 2019), encompassing processes or events that a!ect the functionality of system components or the system as a whole. Keeping engineering systems operational and available under limited resources highlights the importance of approaches such as Prognostics and Health Management (PHM), which will be later explained. PHM is gaining popularity in various industries because it improves sustainability by considering environmental, social, and economic factors, aiming to use resources e"ciently and positively impact the environment. The main goal of PHM is to use models and data to spot unusual behaviours and problems, diagnose issues, and anticipate future performance. This aids in developing optimal strategies for e!ective system management. Within the context of the PrimaVera project (https://primavera-project.com/), this work investigates key aspects of PHM through the lenses of ‘Reliability Modelling’, ‘Markov Process-based Prognostics’, and ‘Maintenance Optimisation’. Before addressing the research gaps detailed in Section 1.3, the next section provides an overview of the relevant literature and the background of the primary methods, models, and algorithms employed in this dissertation.
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