UNIVERSITY OF TWENTE Abstract Faculty of Electrical Engineering, Mathematics and Computer Science Formal Methods and Tools Doctor of Philosophy Reliability and Maintenance for Engineering Systems: Fault Trees, Degradation Modelling and Maintenance Optimisation by Lisandro Arturo Jimenez Roa Modern infrastructures, machines, and manufacturing processes require e!ective management through sustainable policies under constrained resources, where determining when and how to intervene becomes crucial. The Prognostics and Health Management (PHM) paradigm provides a systematic framework for leveraging data collection and computational models, supporting the management of virtually any engineering component or system. This dissertation delves into three key aspects of PHM: Reliability Modelling, Markov Process-based Prognostics, and Maintenance Optimisation. Data-driven techniques are crucial in these areas, enhancing the automation of model development and deployment. Part I centres on Reliability Modelling, specifically the automatic inference of Fault Tree (FT) models. Traditionally, graph-based models like FTs are manually constructed through iterative collaboration between system experts and FT modellers. However, this manual approach is prone to human error and may result in incomplete models. With the increasing data availability, methodologies that attempt to automate this process, discover patterns and reduce dependency on manual intervention have gained significant relevance. Thus, in Part I of this dissertation, we focus on how to obtain e!cient and compact Fault Tree models from failure datasets in a robust and scalable manner. For this, we propose for the first time, using Multi-Objective Evolutionary Algorithms (MOEAs) to automatically infer FT models and frame the optimisation as a multi-objective task. This resulted in the FT-MOEA algorithm (Chapter 2), focusing on three optimisation metrics, including FT size and accuracy-related metrics. FT-MOEA consistently produced compact FT structures, but faced scalability issues. To address this, we developed the SymLearn toolchain (Chapter 3), which uses a ‘divide-and-conquer’ approach by identifying modules and symmetries in the
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