Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets Saleh, Ali Chiachío Ruano, Manuel Fernández Salas, Juan Petri net Reinforcement learning Q-learning Offshore wind turbines Condition-based maintenance With the emerging monitoring technologies, condition-based maintenance is nowadays a reality for the wind energy industry. This is important to avoid unnecessary maintenance actions, which increase the operation and maintenance costs, along with the costs associated with downtime. However, condition-based maintenance requires a policy to transform system conditions into decision-making while considering monetary restrictions and energy productivity objectives. To address this challenge, an intelligent Petri net algorithm has been created and applied to model and optimize offshore wind turbines’ operation and maintenance. The proposed method combines advanced Petri net modelling with Reinforcement Learning and is formulated in a general manner so it can be applied to optimize any Petri net model. The resulting methodology is applied to a case study considering the operation and maintenance of a wind turbine using operation and degradation data. The results show that the proposed method is capable to reach optimal condition-based maintenance policy considering maximum availability (equal to 99.4%) and minimal operational costs. 2023-01-26T08:04:28Z 2023-01-26T08:04:28Z 2022-12-05 journal article Ali Saleh... [et al.]. Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets, Reliability Engineering & System Safety, Volume 231, 2023, 109013, ISSN 0951-8320, [https://doi.org/10.1016/j.ress.2022.109013] https://hdl.handle.net/10481/79349 10.1016/j.ress.2022.109013 eng info:eu-repo/grantAgreement/EC/H2020/859957 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier