Maintenance optimization of floating offshore wind turbines: digital twins and stochastic modeling for uncertainty management
Year: 2025
Type: conference-paper
Abstract: Offshore wind turbines (OWTs) face high maintenance costs due to harsh and uncertain environmental conditions.Traditional maintenance strategies-whether fixed-schedule, failure-based, or risk-based-often fail to capture real-time weather impacts or gradual degradation under normal conditions.This study proposes a risk-informed, condition-based predictive maintenance strategy that incorporates environmental uncertainty to reduce costs and improve decision-making.The approach transforms the probability of failure of critical components into a condition factor used to trigger maintenance actions.Two key aspects are evaluated: (1) the cost-effectiveness of collectively maintaining spatially proximate turbines, and (2) the performance of the proposed predictive strategy versus traditional fixed-interval maintenance.Results show that grouped maintenance significantly reduces logistical costs, and the predictive strategy lowers the number of interventions while minimizing downtime, outperforming fixed-schedule approaches.
Authors: X. Zhang, A. Noshadravan
Institution: Texas A&M University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411488962