177 Chapter 8 Discussion This doctoral thesis, divided into three parts, addresses relevant aspects of the Prognostics and Health Management (PHM) paradigm for engineering applications. This chapter discusses: Reliability Modelling, specifically the data-driven inference of fault tree models (Section 8.1); Markov Process-based Prognostics, particularly multi-state deterioration modelling (Section 8.2); and Maintenance Optimisation using Deep Reinforcement Learning (Section 8.3). 8.1 Reliability Modelling: Data-driven Inference of Fault Tree models Overview of the research problem in Part I One of the main challenges in reliability modelling is building the model itself, and this is particularly true for graph-based methods such as Fault Tree Analysis (FTA), where constructing Fault Tree (FT) models typically involves an iterative process between experts and FT modellers, which may be prone to human error. Developing algorithms to automate this process and identify overlooked patterns is crucial. Recap on contributions in Part I In Part I of this dissertation, we explored, for the first time, Multi-Objective Evolutionary Algorithms (MOEAs) to automatically infer FTs from failure datasets. In the domain of reliability modelling, our contributions are three-fold: 1. The FT-MOEA algorithm (Chapter 2), based on an MOEA, accounts for three optimisation metrics, including minimising FT size and accuracy-related error metrics. With FT-MOEA, we can consistently obtain compact FT structures. Data and implementations are available at zenodo.org/record/5536431. 2. The SymLearn toolchain (Chapter 3) employs a “divide and conquer” strategy, exploiting symmetries and modules that may be present in the failure dataset.
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