Ultrasound-based identification of damage in wind turbine blades using novelty detection

Abstract

Among the renewable energy sources, wind power generation presents competitive costs and high installation potential in many countries. Ensuring the integrity of the generation equipment plays an important role for reliable energy production. Therefore, nondestructive test procedures are required, especially for turbine blades, which are subject to severe operational conditions due to phenomena such as lightning strikes, mechanical stress, humidity and corrosion. Nondestructive ultrasonic test techniques are commonly applied in their predictive maintenance. This work proposes the use of novelty detection methods combined with nondestructive ultrasound testing to identify structural problems in wind turbine blades. Ultrasound signals are preprocessed using both, wavelet denoising and principal component analysis. Novelty detection deals with the one-class classification problem, when only the normal condition signatures are required for the classification system design. For the nondestructive test of turbine blades, this is an interesting paradigm because it is not always possible to obtain test samples from all of the existing flaw conditions. Our experimental results indicate the efficiency of the proposed method.