A Risk-Based Reliability Model for Offshore Wind Turbine Foundations Using Underwater Inspection Data

Advancements in offshore wind energy have augmented concerns related to the structural integrity and reliability of turbine foundations as they interface with the energy conversion systems and the hostile marine environment. Conventional deterministic approaches to the design and maintenance of foundations do not typically account for the uncertainties posed by the marine biofouling, corrosion, and fatigue accumulation in due course over time. This work presents a risk-based reliability model that assesses the underwater inspection integrity of offshore wind turbine foundations to derive improved probabilistic long-term stability evaluation. The model uses inspection data from ROV imagery, cathodic protection, and corrosion depth profiles to derive salient statistical relationships among structural performance and degradation processes. Thickness of marine growth, rate of corrosion, and fatigue crack initiation in the turbine operational life model are treated as stochastic phenomena. The model integrates finite damage and vibration response (FDVR) analysis with standard Integrity Element-Imager (IX) simulations and geospatial information systems (GIS) to derive site-based risk analytics within a wind farm. The model also enables failure probability analysis considering specific environmental loads in the site, like wave and biofouling, under different operational conditions. The projected outcomes lead to the development of a decision framework based on reliability analysis for targeted maintenance and life extension planning for inspections. Unlike traditional threshold-based methods, the risk-based approach shifts the management paradigm toward examining the greatest probability of interrelated system failure. It economically improves the management of offshore wind assets by correlating actual inspection data to predictive reliability models, thus reducing uncertainties and enhancing operational reliability. The fusion of theoretical probabilistic models, risk analysis, and remote digital inspection data consolidates the resilience of offshore wind infrastructure, advancing the development of renewable energy to make it safer, more sustainable, and socially responsible.