Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for hydroelectric power.
This paper proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, followed by the application of a K-Nearest Neighbor (KNN) machine learning algorithm for fault detection and severity classification. The framework's effectiveness is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that pitch imbalance faults can be accurately detected 100% of the time and classified based upon severity more than 97% of the time.