failure dataset, breaking the inference problem into smaller tasks. Additionally, to improve robustness and scalability, the FT-MOEA-CM extension (Chapter 4) includes additional metrics from the confusion matrix. Our approaches in Part I contribute to automating the construction of FT models, revealing compact structures, which can help uncover relationships between basic and intermediate events, providing valuable patterns for asset managers to improve reliability modelling. Part II focuses on Markov Process-based Prognostics, specifically to model the stochastic deterioration of sewer mains. Sewer systems are critical to social welfare but pose significant challenges due to their extensive scale and limited capacity to monitor the entire network. Accurate modelling of the deterioration profile is crucial for optimising inspections and maintenance, thereby enhancing the reliability and availability of the system. Di!erent types of deterioration models are discussed in the literature, ranging from physics-based to data-driven approaches, each with distinct advantages and limitations. In Part II of this dissertation, we address how and to what extent it is possible to accurately model Multi-State Deterioration with applications in sewer mains. For this, we focus on Markov chains, widely used to model stochastic sequences through states and transitions. Since the 1990s, they have been applied to represent damage severity levels in sewer mains using inspection data from Closed Circuit Television cameras. Nonetheless, further evaluation of their assumptions and properties is required. We present a case study of a Dutch sewer network (Chapter 5), starting with Discrete-Time Markov Chains for deterioration modelling and examining two Markov chain structures. Given challenges in the data such as interval-censoring, advanced analysis was necessary, for this in Chapter 6, we implement the Turnbull estimator for non-parametric analysis to establish a ground truth. Although both homogeneous and inhomogeneous-time Markov chains are employed for sewer mains deterioration, no prior studies have compared their performance on the same dataset. Chapter 6 addresses this by demonstrating that inhomogeneous-time Markov chains are more versatile at capturing non-linear stochastic behaviour, while also highlighting issues like overfitting that reduce predictive accuracy. Part II provides a real-world case study, emphasising the need to critically evaluate modelling assumptions to enhance deterioration modelling of sewer mains using Markov chains. Finally, Part III focuses on Maintenance Optimisation of sewer mains, where obtaining optimal maintenance policies for such components is a complex task. This complexity arises, among others, from the system’s scale, availability of adequate data, and simplifications in the deterioration model. Among the techniques available, Reinforcement Learning (RL) remain largely unexplored for devising strategic maintenance actions in sewer mains. Thus, in Part III of this dissertation, we focus on how to devise near-optimal maintenance strategies for components with Multi-State Deterioration such as sewer mains using Deep Reinforcement Learning.
RkJQdWJsaXNoZXIy MjY0ODMw