AI-driven Degradation Analysis of Oscillating Water Column Turbines under Uncertainty

This work addresses the important and yet relatively unexplored topic of degradation analysis of wave energy converters and more specifically Oscillating Water Column Turbines (OWCTs). This is achieved by developing mathematical models to simulate turbine degradation factors over time while considering environmental variability. Specifically, these factors are then combined multiplicatively to portray the overall health parameters of an OWCT system. Additionally, sensor measurements are generated based on these health parameters, encompassing various turbine-related and environmental variables such as vibration, temperature, pressure, acceleration, strain, flow, torque, rotation, wind speed, wind direction, air temperature, humidity, water temperature, solar radiation, wave height, wave frequency, wave direction, current velocity, water depth, salinity, biofouling, and seabed conditions. The generated dataset facilitates degradation analysis, enabling prognostic under uncertainty quantification. A set of adaptive time series along with small-scale learning algorithms are employed to analyze the dataset. Additionally, a wide range of metrics, and coefficient of determination, are utilized in a cross-validation scheme for generalizability evaluation. Moreover, confidence interval-based uncertainty quantification is integrated. This in-depth analysis sheds light on degradation patterns and their complexity, laying the basis for future studies into prognostics and health management of wave energy converter systems.