For offshore wind turbines (OWTs), effective maintenance decision-making depends on the timely and intelligent anticipation of developing faults. Without requiring the installation of additional sensors, failure-related information can be extracted from the widely available Supervisory Control and Data Acquisition (SCADA) system. This thesis presents an integrated deep learning framework designed to interpret high-dimensional, unlabeled, and often low-quality SCADA data for fault diagnosis and Remaining Useful Life (RUL) estimation.

The framework identifies historical failure events through reconstruction-based anomaly detection and the construction of a health indicator. By clustering detected anomalies, associated failure modes are inferred, allowing classification of future fault types. Using the estimated moments of failure as guidance, it then learns degradation trends in the reconstruction feature space and performs RUL prediction.

Given the complexity of offshore environments and the unpredictable nature of wind turbine faults, the framework is first validated in a controlled setting using NASA's C-MAPSS simulated aircraft engine dataset. The results are competitive and align well with those reported in related studies. Subsequent application to real-world OWT SCADA data demonstrates the practical feasibility of the approach. However, challenges such as data imbalance, obscured features due to SCADA data quality issues, and propagation of errors between model components complicate implementation and reduce prediction reliability.

Despite these challenges, the proposed framework successfully extracts health-relevant insights, enabling predictive maintenance optimization and contributing to more informed data-driven decision-making in offshore wind operations.

Offshore wind farms face significant operational and financial challenges due to weather-related uncertainties that disrupt short-term maintenance planning. Operational decisions are critical to maintaining turbine performance and ensuring technician safety. When such decisions fail to effectively account for short-term risks like adverse weather and human limitations, they can result in failed maintenance attempts, prolonged downtime, increased CO2 emissions, and substantial financial losses.

This thesis develops a probabilistic decision-support model designed to improve the reliability, cost efficiency, safety, and sustainability of offshore wind maintenance operations by incorporating short-term weather forecasts, vessel operability constraints, and crew safety—specifically, the risks of seasickness during transit to the offshore wind farm (OWF) and unsafe technician transfer from vessel to turbine platform.

The model was evaluated through a 10-year case study simulating 19,090 minor repair tasks executed by a Crew Transfer Vessel (CTV), drawn from 75 maintenance schedules across 10 turbines, for a potential OWF site located approximately 40 km offshore from Cabo Silleiro, Spain, in the Atlantic Ocean. Hindcast weather data (2001–2010) for the offshore location were used to compare two maintenance scheduling strategies: a standard industry approach using deterministic vessel operability thresholds and fixed crew size (Case 1), and the proposed probabilistic model integrating transit and transfer uncertainties, a cost-loss decision framework and dynamic crew size optimization (Case 2).

Mission feasibility was determined using the combined probability of mission success, calculated as the product of (i) transfer success probability, based on wave height, wind speed, and wave period, and (ii) transit success probability, modeled using a Binomial distribution, to estimate the likelihood that enough technicians remain healthy (not seasick) upon arrival at the turbine. This probability was derived using Motion Sickness Incidence (MSI) empirical values and sea state conditions. A mission was attempted only if this combined probability exceeded the cost–loss ratio, which compares the cost of a mission attempt with the expected loss from failure. The two strategies were evaluated based on key performance metrics, such as combined probability of mission success, expected operational costs, and environmental impact (CO2 emissions). A sensitivity analysis further confirmed the model’s robustness
across different weather conditions by testing three scenarios: Scenario 1 (20% harsher than hindcast weather), Baseline scenario (hindcast weather), and Scenario 2 (20% more favorable weather).

Results showed that Case 2 improved the average combined probability of mission success to 68.7%, compared to 43.3% in Case 1 (a 58% increase), and reduced expected financial losses by approximately $540,000 over the planning horizon. It also reduced CO2 emissions from re-attempted trips by up to 45.8%. Although Case 2 introduced an average delay of 35 days in baseline conditions by avoiding risky missions, it consistently maintained a success rate above 67% even in the worst-case scenario, demonstrating reliable and robust performance across diverse weather conditions.

The study acknowledges several limitations. Due to confidentiality constraints, real-world maintenance records were not available for validation. Seasickness was estimated using empirical MSI values under simplified assumptions. The model also assumes a fixed start time for daily shift, a single task per day using one CTV, and does not currently account for task prioritization. Despite these constraints, the framework is adaptable and suitable for real-world implementation.

Future research could focus on validating the model using industry maintenance data, developing a new metric for seasickness estimation, and extending the scheduling logic to support multiple vessels and dynamic shift planning. Broader applications could include SOV-based operations, major repairs, and prioritization of critical tasks.

In conclusion, this thesis presents a practical, flexible, and sustainability-oriented framework for offshore wind maintenance planning. By enabling smarter, risk-informed decisions, the model supports reduced unnecessary vessel trips, lower emissions, and more efficient use of resources.

With the development of society and the requirements for clean energy, offshore wind farms (OWFs) that can generate steady and continuous electricity have been built. With harsher weather conditions and more powerful wind resources, the wind turbines at offshore wind farms are significantly larger than those at onshore wind farms. The complex weather conditions and mechanical structure of wind turbines pose problems for their operation and maintenance (O&M).
The cost of vessel chartering significantly contributes to the overall cost of operating and maintaining an offshore wind farm. By selecting the optimal fleet mix for executing the maintenance, the vessel chartering cost can be reduced, reducing the operation and maintenance costs of the offshore wind farm. This reduces the levelized cost of energy from the offshore wind farm. This report developed a simulation and optimization model based on mixed integer linear programming to determine the opti-mal fleet mix for executing the maintenance tasks by minimizing the vessel acquisition cost. The Monte Carlo simulation is implemented to statistic an optimal strategy for chartering the vessels.
Chapter 1 introduces the offshore wind farm (OWF) and its operation and maintenance (O&M) activi-ties. Chapter 2 provided a literature review on the latest progress of the offshore wind farm’s operation and maintenance. Chapter 3 provides a simulation model for component wear, maintenance require-ment generation, and maintenance task execution. The process of optimization is explained in detail. Chapter 4 presents the mathematical model of the optimizer, which arranges the vessels and executes maintenance tasks. Chapter 5 presents a case study based on the latest progress data. The Monte Carlo simulation yields the optimal initial purchased fleet mix derived based on the Monte Carlo simu-lation. Finally, Chapter 6 gives the conclusion and recommendations for future studies.