Structural health and intelligent monitoring of wind turbine blades with a motorized telescope

Abstract

Currently, wind energy plays a fundamental role in the process of generating energy in a sustainable and environmentally friendly manner. However, their infrastructures require ongoing maintenance tasks that involve considerable risk. This is why a predictive maintenance system for the surface inspection of wind turbine blades based on machine learning techniques has been developed. Specifically, convolutional neural networks have been applied to detect and classify turbines and their blades, as well as the surface defects that may appear on them. The system comprises a mobile application that makes use of a telescope to take pictures with certain precision, a computing edge node responsible for processing the images that are captured, and a motorized mount that allows the telescope to move. The objective of this open-source project is to detect and classify different surface defects on the blades of wind turbines and carry out the maintenance of these infrastructures. The system is responsible for undertaking a complete sweep of the surface of the turbine blades in an autonomous way and finally presents the defects found to the user. The deep neural networks also help the system to decide which movements the motorized mount has to make together with the telescope to perform the inspection. Accuracies of around 97% for label predictions and 90% for bounding box coordinate predictions have been achieved for the convolutional deep learning models. Two possible approaches have been considered for the project: the first is to carry out all the necessary computation on a mobile phone to have a portable solution, and the second option considers a edge node to balance the load and thus not overload the mobile device. Tests show that the edge node approach gives better results overall. The proposed system for detecting surface damage on blades was experimentally validated on a wind farm.