Unsupervised learning-based image recovery and diagnosis method for blade attachment of marine current turbine

Abstract

Marine current energy is a clean and renewable resource that is an available energy source with future power generation potential. However, complex subsea conditions can cause blade failures in marine current turbines (MCTs); therefore, the fault diagnosis of MCTs is essential to ensure their good operation. Image-based diagnosis is a nondestructive approach. To improve the efficiency of using human capital, this study uses the unsupervised learning network GANomaly trained only with normal samples to diagnose the leaf attachment of MCTs. Marine biofouling is a lengthy process, and according to the literature, the blades were contaminated by a 1.1-mm-thick layer of lithium lubricating grease, therefore, this study uses ropes to simulate this attachment. First, we address the problem of blurred underwater images collected using a multiscale retinex enhancement algorithm with color recovery to form a new dataset. GANomaly is modified to diagnose MCT blades with different attachment degrees. The experimental results show that the accuracy of the enhanced dataset is improved by 1.75 %, and the accuracy of the improved GANomaly model reaches 91.23 %, thereby enhancing the efficiency of the fault diagnosis of MCTs.