Inspection and maintenance planning for offshore wind structural components: integrating fatigue failure criteria with Bayesian networks and Markov decision processes

Abstract

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 decisionmaking 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.

Knowledge engineering for wind energy

“…Clere Smithe, 2023), formalising co-design problems (Zardini et al, 2021), data management Johnson et al, 2012), and creation of digital twins (Qi et al, 2022). For sequential decisionmaking, the value of information theory and partially observable Markov decision process (POMDP) model has been used to express relationship between an agent and its dynamic system environment along with related uncertainties (Papakonstantinou and Shinozuka, 2014;Andriotis et al, 2021), with applications in a wind energy context Liang et al, 2022;Hlaing et al, 2022). It should be noted that many of the above-mentioned formalisms have not yet received widespread adoption and therefore often lack practical technological implementations (see Sect.…”

Knowledge Engineering for Wind Energy

“…Clere Smithe, 2023), formalising co-design problems (Zardini et al, 2021), data management (Spivak, 2012;Johnson et al, 2012), and creation of digital twins (Qi et al, 2022). For sequential decision making, value of information theory and Partially Observable Markov Decision Process (POMDP) model has been used to express relationship between an agent and its dynamic system environment along with related uncertainties (Papakonstantinou and Shinozuka, 2014;Andriotis et al, 2021), with applications in wind energy context (Morato et al, 2022;Liang et al, 2022;Hlaing et al, 2022). It should be noted that many of the above-mentioned formalisms have not yet received wide-spread adoption and therefore often lack practical technological implementations (see Section 4), as opposed to DL and FOL.…”

Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks

“…Combined with engineering models, manual and/or robotic inspections can be conducted in order to reduce the uncertainties associated with deterioration estimations, hence supporting more rational and informed maintenance decisions. 1,2 With the advent of modern sensor technologies, monitoring systems are increasingly being deployed with the objective of continuously monitoring the deterioration experienced by offshore wind structures, thus also enabling decision-makers to make timely and informed decisions. 3,4 For example, fatigue load monitoring through strain gauges provides valuable information that can be used to estimate the remaining useful fatigue lifetime [5][6][7] and/or to update probabilistically modeled time-varying deterioration mechanisms.…”