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This study introduces a multi-strategy collaborative optimization approach to enhance maintenance decision-making in offshore wind turbines amidst uncertainties in lifetime prediction. In contrast to conventional deterministic strategies, the proposed method integrates uncertainty directly into the optimization framework, ensuring a thorough balance between cost and risk considerations. Initially, a dynamic probabilistic modeling scheme is employed to represent the time-varying nature of prediction errors. By developing a residual life prediction error model grounded in time-dependent probability distributions, this approach effectively captures the evolving stochastic characteristics inherent in degradation forecasts. Second, a cumulative maintenance risk quantification model is developed within the Conditional Value-at-Risk(CVaR) framework. This model addresses the asymmetric impacts of prediction errors on maintenance outcomes and offers a comprehensive measure of risk exposure. Monte Carlo sampling is utilized to generate sets of uncertain scenarios, facilitating robust estimation of potential loss distributions. Leveraging these models, a hierarchical maintenance strategy is formulated that integrates corrective, preventive, and opportunistic maintenance into a cohesive framework. To optimize both maintenance costs and operational risks throughout the turbine's life cycle, a multi-objective maintenance optimization model is established. The Nondominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ) is employed to solve this model, facilitating efficient exploration of the Pareto frontier and enabling coordinated optimization of strategy selection and decision thresholds. Case study results confirm the effectiveness of the proposed approach, demonstrating that the optimized strategies significantly reduce maintenance costs while precisely quantifying and mitigating operational risks. Notably, the integration of probabilistic modeling with scenario-based risk evaluation ensures that maintenance policies remain robust against various sources of uncertainty. In conclusion, the proposed method not only enhances the economic efficiency of offshore wind turbine operations and maintenance but also establishes a rational trade-off between cost and risk. These contributions provide a systematic and practical decision-support framework that can be applied to other complex engineering systems facing similar uncertainty-driven challenges.
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Basic Information:
DOI：10.13637/j.issn.1009-6094.2025.1228
China Classification Code:TM315
Citation Information:
[1]Li Yanan,Zhang Xinsheng,Wu Chunyang ,et al.Optimizing opportunistic maintenance for offshore wind turbines: addressing uncertainty in life prediction[J].Journal of Safety and Environment,2026,26(07):2514-2526.DOI:10.13637/j.issn.1009-6094.2025.1228.
Fund Information:
陕西省创新能力支撑计划软科学项目(2024ZC-YBXM-010); 陕西省教育厅科学研究计划项目人文社科专项(24JK0131)
2026-01-09
2026-01-09
2026-01-09