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

Aug 5, 2026

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Wind energy is crucial for sustainable electricity generation, yet wind-turbine infrastructure demands continuous maintenance under significant operational risk. A predictive maintenance framework is presented for automated surface inspection of wind-turbine blades using machine learning. Convolutional neural networks detect and classify turbines and blades, localize surface defects, and guide inspection movement. The system integrates a mobile application with a motorized telescope, an edge computing node for image processing, and autonomous scanning, validated on a wind farm with high localization and label accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Maintenance risk and failure detection

Blade importance and inspection challenges

Proposed autonomous monitoring approach

Structural health and intelligent monitoring of wind turbine blades with a motorized telescope Alejandro Carnero ITIS Software University of Malaga Malaga, Spain Cristian Mart´ın ITIS Software University of Malaga Malaga, Spain Manuel D´ıaz ITIS Software University of Malaga Malaga, Spain 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. Index Terms—structural health monitoring, wind turbines, motorized telescope, machine learning I. INTRODUCTION Today, the world of wind energy continues to grow exponentially, with numerous wind turbines in operation on a regular basis. These infrastructures are located in environments where conditions are complex and therefore these experience numerous failures over time. Due to this and the large size of the turbines, their maintenance carries a lot of risk [1] [2] . Therepair of these systems supposes an increase in their lifetime but in turn, late failure detection increases their operating cost. For this reason, it is important to detect the problems early and thus reduce the possibility of a major failure that increases the operating cost of the turbines [3] . ICMLA 2022. DOI: [https://doi.org/10.1109/ICMLA55696.2022.00022](https://doi.org/10.1109/ICMLA55696.2022.00022) The blades are a fundamental element of wind turbines. They are expensive, accounting for 20% of the total cost [4] [5] and slightly less than 15% of turbine failures approximately [6] . Some of the consequences of blade failures are considerable economic losses due to the repair or replacement, unplanned system shutdowns, and other types of accidents that can endanger human and environmental safety [7] . Consequently, it is important to develop a system that monitors the structural health of wind turbines safely and efficiently in order to avoid these problems. There are different types of traditional wind turbine inspections. The use of various sensors has allowed the internal state of the turbines to be monitored, however, for their external state, there are still no methods that are safe and efficient enough, which are needed. For external inspection, the

FAQs

What problem does the proposed system address for wind turbines?

It targets the risk and cost of maintenance by enabling early detection and classification of surface defects on wind-turbine blades using an automated inspection workflow.

How does the system capture and analyze blade surfaces?

A mobile application coordinates a motorized telescope to capture precisely positioned images, while an edge computing node processes the images and runs deep learning models to detect and localize defects.

What deep learning methods are used and how accurate are they?

Convolutional neural networks are used for label prediction and bounding box coordinate prediction of surface defects, achieving about 97% label accuracy and about 90% coordinate prediction accuracy.

Aug 5, 2026

Upload by Aria