Convolutional neural network–based structural health monitoring framework for wind turbine blade

Abstract

Wind energy is a renewable energy source with a significant impact on the date for harnessing it. Different types of wind turbines (WTs) are being built to generate affordable, dependable, and eco-friendly renewable wind energy. Over time, cracks are found to be developed in the blades of the WTs, which is considered the most common type of damage that ultimately causes the catastrophic failure of the structures. This study aims to develop a structural health monitoring (SHM) framework for localizing cracks of a WT blade using a convolutional neural network (CNN)–based deep learning algorithm. With that objective, the NACA (National Advisory Committee for Aeronautics) 63-412 profile (WT blade) of length 29 m was modelled in finite element analysis (FEA) ANSYS 2022 R2. A crack is introduced to the model by making a groove at different blade locations. The mode shape and natural frequencies are obtained and validated with those reported in the literature. Further, the blade is excited by an impact load applied at the tip of the blade, and acceleration time histories are collected. The acceleration time history data are converted to scalogram images and fed into the CNN algorithm for damage classification and localization. In this study, the concept of class activation maps is also utilized for visual representations of the input images’ areas that significantly influence a class’s classification score. The classified results show that using CNN can successfully localize the region of the cracks and motivate us to examine it on a laboratory-based model.