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Inspection and maintenance planning for offshore wind structural components: integrating fatigue failure criteria with Bayesian networks and Markov decision processes
Exposed to the cyclic action of wind and waves, offshore wind structures are subject to fatigue deterioration processes throughout their operational life, therefore constituting a structural failure risk. In order to control the risk of adverse events, physics-based deterioration models, which often contain significant uncertainties, can be updated with information collected from inspections, thus enabling decision-makers to dictate more optimal and informed maintenance interventions. The identified decision rules are, however, influenced by the deterioration model and failure criterion specified in the formulation of the pre-posterior decision-making problem. In this paper, fatigue failure criteria are integrated with Bayesian networks and Markov decision processes. The proposed methodology is implemented in the numerical experiments, specified with various crack growth models and failure criteria, for the optimal management of an offshore wind structural detail under fatigue deterioration. Within the experiments, the crack propagation, structural reliability estimates, and the optimal policies derived through heuristics and partially observable Markov decision processes (POMDPs) are thoroughly analysed, demonstrating the capability of failure assessment diagram to model the structural redundancy in offshore wind substructures, as well as the adaptability of POMDP policies.
Inspection and maintenance planning for offshore wind structural components: integrating fatigue failure criteria with Bayesian networks and Markov decision processes
Exposed to the cyclic action of wind and waves, offshore wind structures are subject to fatigue deterioration processes throughout their operational life, therefore constituting a structural failure risk. In order to control the risk of adverse events, physics-based deterioration models, which often contain significant uncertainties, can be updated with information collected from inspections, thus enabling decision-makers to dictate more optimal and informed maintenance interventions. The identified decision rules are, however, influenced by the deterioration model and failure criterion specified in the formulation of the pre-posterior decision-making problem. In this paper, fatigue failure criteria are integrated with Bayesian networks and Markov decision processes. The proposed methodology is implemented in the numerical experiments, specified with various crack growth models and failure criteria, for the optimal management of an offshore wind structural detail under fatigue deterioration. Within the experiments, the crack propagation, structural reliability estimates, and the optimal policies derived through heuristics and partially observable Markov decision processes (POMDPs) are thoroughly analysed, demonstrating the capability of failure assessment diagram to model the structural redundancy in offshore wind substructures, as well as the adaptability of POMDP policies.
Inspection and maintenance planning for offshore wind structural components: integrating fatigue failure criteria with Bayesian networks and Markov decision processes
Hlaing, Nandar (Autor:in) / Morato, Pablo G. (Autor:in) / Nielsen, Jannie S. (Autor:in) / Amirafshari, Peyman (Autor:in) / Kolios, Athanasios (Autor:in) / Rigo, Philippe (Autor:in)
Structure and Infrastructure Engineering ; 18 ; 983-1001
03.07.2022
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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