Inspection Of Wind Turbine Blades Research Articles

Drone-based inspection of wind turbine blades: a comparative study of deep learning models

Maintaining wind turbine blades is a challenging task, often marked by high costs, safety risks, time inefficiency, and the possibility of incorrect diagnosis. A promising approach to support preventive maintenance involves the use of drones and deep learning for inspection and early fault detection. This paper offers a comparative study of five advanced deep learning models: Residual Networks (ResNet), Inception Networks (InceptionV3), YOLO (You Only Look Once), EfficientNet, and Vision Transformers (ViTs) applied to the DTU Drone Inspection Images of Wind Turbine dataset. The objective is to determine the most effective model for detecting defects in wind turbine blades, which is critical for ensuring the efficiency and safety of wind energy systems. The models were assessed using key performance metrics, such as accuracy, precision, recall, F1-score, and inference time. EfficientNet emerged as the top performer with the highest accuracy and balanced efficiency, while YOLO demonstrated the fastest inference time, making it ideal for real-time applications. ResNet and InceptionV3 also performed well, particularly in detecting subtle defects, whereas ViTs showed strong capabilities in capturing complex global features despite longer processing times. The findings provide valuable insights for selecting appropriate deep learning models in drone-based wind turbine inspections, contributing to more effective and reliable maintenance practices.

Image acquisition technology for unmanned aerial vehicles based on YOLO - Illustrated by the case of wind turbine blade inspection

Wind energy, as a renewable energy source, is becoming increasingly important. The maintenance and damage detection of wind turbine blades are particularly crucial. For this purpose, the study aims to optimize the You Only Look Once (YOLO) processing algorithm for drone images to improve the detection efficiency. Firstly, the damage images captured by drones are preprocessed and optimized, including deblurring, noise reduction, and image enhancement. Subsequently, the YOLOv5 model is improved in terms of structure and regression function, and a novel damage detection model is proposed. The research results indicated that the minimum loss function value of the improved model was 2.75, the average accuracy was 95 %, and the highest intersection over union was 91 %. After simulation testing, the detection effect of this model on abrasion, crackle, edge cracking, and coating peeling images was significantly better than other models in the same series. Its average time was as short as 2.43 s, reaching a maximum frame rate of 35.46. From this, the combination of drone image technology and improved image processing algorithm has a positive impact on improving the operational efficiency and safety of wind turbine blades. Compared with the traditional methods, the proposed model has significant advantages in terms of accuracy and real-time performance of damage detection, providing a new technical means for efficient maintenance of wind turbines. Meanwhile, the method shows high robustness and reliability in different types of damage detection, demonstrating the extensive potential in practical applications.

Harnessing the power of thermal imagery and visual inspection- a mean for reliable damage detection of wind turbine rotor blades

Generation of green electricity as part of the energy transition is leading to a growing market in the wind energy sector all over the world. Maintenance and inspection are key to the reliability, safety and efficiency of wind turbines, the regular maintenance of rotor blades focuses on damage such as erosion on the leading edge of the profile, delamination and thermal cracks due to lightning strikes. To date, visual inspection by technicians (climbers) has been the state of the art and it is time consuming besides posing safety risk for themselves. Recently, drone-based inspections using visual cameras have become more common, enabling fast, reliable and cost-effective inspections. However, no internal damage to the rotor blades can be detected during such an inspection. Thermography is a recognised method for detecting damage beneath the surface of an object, which has been promoted and further developed at BAM for years. To enhance the accuracy and reliability of wind turbine blade inspection, the fusion of thermal and visual data has emerged as a promising technique. Here, a drone-based multisensory system is being presented that combines thermal imagery, high-resolution visual images and a three-dimensional image obtained from 3D Laser scanner to represent a comprehensive view of the blades' internal and external conditions. Besides this, flight data obtained from IMU, GPS and LiDAR sensors will be used for enhancing the inspection data. Thus, fusion of images enables a more detailed analysis of potential defects and this not only improves the overall effectiveness of the inspection process but also provides windmill operators a new dimensions for data analysis and decision making.

Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Inspection of Wind Turbine Blades Based on Modified Thermoelastic Stress Analysis Using Thermal Detection of Infrared Radiation

Inspection of Wind Turbine Blades Based on Modified Thermoelastic Stress Analysis Using Thermal Detection of Infrared Radiation

Towards accurate image stitching for drone-based wind turbine blade inspection

Accurate image stitching is crucial to wind turbine blade visualization and defect analysis. It is inevitable that drone-captured images for blade inspection are high resolution and heavily overlapped. This also necessitates the stitching-based deduplication process on detected defects. However, the stitching task suffers from texture-poor blade surfaces, unstable drone pose (especially off-shore), and the lack of public blade datasets that cater to real-world challenges. In this paper, we present a simple yet efficient algorithm for robust and accurate blade image stitching. To promote further research, we also introduce a new dataset, Blade30, which contains 1,302 real drone-captured images covering 30 full blades captured under various conditions (both on- and off-shore), accompanied by a rich set of annotations such as defects and contaminations, etc. The proposed stitching algorithm generates the initial blade panorama based on blade edges and drone-blade distances at the coarse-grained level, followed by fine-grained adjustments optimized by regression-based texture and shape losses. Our method also fully utilizes the properties of blade images and prior information of the drone. Experiments report promising accuracy in blade stitching and defect deduplication tasks in the vision-based wind turbine blade inspection scenario, surpassing the performance of existing methods.

A Survey on Non-Destructive Smart Inspection of Wind Turbine Blades Based on Industry 4.0 Strategy

Wind turbines are known to be the most efficient method of green energy production, and wind turbine blades (WTBs) are known as a key component of the wind turbine system, with a major influence on the efficiency of the entire system. Wind turbine blades have a quite manual production process of composite materials, which induces various types of defects in the blade. Blades are susceptible to the damage developed by complex and irregular loading or even catastrophic collapse and are expensive to maintain. Failure or damage to wind turbine blades not only decreases the lifespan, efficiency, and fault diagnosis capability but also increases safety hazards and maintenance costs. Hence, non-destructive testing (NDT) methods providing surface and subsurface information for the blade are indispensable in the maintenance of wind turbines. Damage detection is a critical part of the inspection methods for failure prevention, maintenance planning, and the sustainability of wind turbine operation. Industry 4.0 technologies provide a framework for deploying smart inspection, one of the key requirements for sustainable wind energy production. The wind energy industry is about to undergo a significant revolution due to the integration of the physical and virtual worlds driven by Industry 4.0. This paper aims to highlight the potential of Industry 4.0 to help exploit smart inspections for sustainable wind energy production. This study is also elaborated by damage categorization and a thorough review of the state-of-the-art non-destructive techniques for surface and sub-surface inspection of wind turbine blades.

A multirobot system for autonomous deployment and recovery of a blade crawler for operations and maintenance of offshore wind turbine blades

AbstractOffshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional human‐based operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic link‐hook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic on‐load attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2‐DOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visual‐based localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proof‐of‐concept system was field tested on a full‐size decommissioned wind‐turbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.

A Robot-Assisted Large-Scale Inspection of Wind Turbine Blades in Manufacturing Using an Autonomous Mobile Manipulator

Wind energy represents the dominant share of renewable energies. The rotor blades of a wind turbine are typically made from composite material, which withstands high forces during rotation. The huge dimensions of the rotor blades complicate the inspection processes in manufacturing. The automation of inspection processes has a great potential to increase the overall productivity and to create a consistent reliable database for each individual rotor blade. The focus of this paper is set on the process of rotor blade inspection automation by utilizing an autonomous mobile manipulator. The main innovations include a novel path planning strategy for zone-based navigation, which enables an intuitive right-hand or left-hand driving behavior in a shared human–robot workspace. In addition, we introduce a new method for surface orthogonal motion planning in connection with large-scale structures. An overall execution strategy controls the navigation and manipulation processes of the long-running inspection task. The implemented concepts are evaluated in simulation and applied in a real-use case including the tip of a rotor blade form.

Leveraging single-shot detection and random sample consensus for wind turbine blade inspection

Leveraging single-shot detection and random sample consensus for wind turbine blade inspection

A Compact Laser Shearography System for On-Site Robotic Inspection of Wind Turbine Blades

Shearography is an optical technique in the field of non-destructive evaluation (NDE) of various materials. Its main advantages are that it is non-contact type and can cover a large area in a single inspection. As a result, although it has been widely acknowledged as an effective technique particularly for NDE of composite materials to detect subsurface defects such as delamination, disbond, cracks and impact damages, the use of shearography for on-site inspection of wind turbine blades (WTBs) has not been reported. This is due to wind causing structural vibration in the WTB. The solution in this paper is to make the shearography sit on the WTB during inspection when the WTB is parked, so that the relative motion between the shearography and the WTB is minimized within the tolerance of the shearography system. The ultimate goal of the solution is to enable a robot assisted shearography system to inspect the WTB on-site. This paper presents the research work on a new shearography design for integration with a robotic climber for on-site WTB inspection. The approach is tested and evaluated in experimental settings, and comparative assessment of the approach with other robotic NDE techniques is carried out. The results demonstrate the potential benefits and suitability of the approach for on-site robotic inspection of WTBs.

Autonomous Wind-Turbine Blade Inspection Using LiDAR-Equipped Unmanned Aerial Vehicle

This paper presents a LiDAR-equipped unmanned aerial vehicle (UAV) performing semi-autonomous wind-turbine blade inspection which includes traversing to the blade tip and back, while keeping constant relative distance and heading to the blade plane. Plane detection is performed applying the RANSAC method on a subset of the gathered pointcloud. Utilizing the relative distance to the inferred plane as well as its normal vector, the UAV is able to maintain a constant distance and heading towards the plane while moving in parallel with it. The proposed procedure performs successful wind-turbine blade inspections with minimal operator involvement. Inspection results include high-resolution blade images as well as a 3D model of the wind-turbine structure. Finally, to show the feasibility of this approach, simulations are performed on a wind-turbine model and experimental results are presented for an outdoor single-blade inspection scenario both on a mock-up setup and a full-scale wind-turbine blade. It is worth noting that the system's adequacy has been fully validated in real conditions on an operational wind farm.

Climbing robots: recent research and emerging applications

Purpose This paper aims to provide details of recent research into robots capable of ascending vertical or near-vertical surfaces and to illustrate how the ability to climb is set to resolve a critical industrial need arising from the growth in renewable energy.Design/methodology/approach Following a short introduction, the first parts of this paper describe a selection of recent research activities that involve innovative concepts and designs. The second part discusses climbing robot developments aimed at the automated inspection, maintenance and repair of wind turbine blades. Brief concluding comments are drawn.Findings Robots that can ascend vertical or near-vertical surfaces are the topic of an extensive and technologically innovative research effort. Many developments take their inspiration from the climbing abilities of living creatures. Drones with the ability to adhere to and climb vertical surfaces are also being developed. Potential applications include inspection, surveillance and search and rescue. Climbing robots are poised to provide a solution to the need to de-man and reduce the cost of inspecting and maintaining composite wind turbine blades.Originality/value This provides an insight into recent innovations in climbing robot concepts and designs and shows how the ability to ascend vertical surfaces is being exploited in the robotic inspection, maintenance and repair of wind turbine blades.

Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach

Regular inspection of wind turbine blades (WTBs), especially the detection of tiny defects, is necessary to maintain safe operation of wind turbine systems. However, current detections are inefficient and subjective because they are conducted merely by human inspectors. An autonomous visual inspection system is proposed in this paper for WTBs, in which a deep learning framework is developed by combining the convolutional neural network (CNN) and the you only look once (YOLO) model. To achieve practically acceptable detection accuracy for small-sized defects on the WTBs, a YOLO-based small object detection approach (YSODA) using a multiscale feature pyramid is proposed by amalgamating features of more layers. To evaluate the proposed YSODA, a database including 23,807 images labeled for three types of defect—crack, oil pollution, and sand inclusion, is developed. Then, the YSODA is with its architecture modified, and is trained, validated, and tested using the images from the database to provide autonomous and accurate visual inspection. After training and testing, resulting detection accuracy reaches 92.7%, 90.7%, and 90.3% for the three types of defect with the average accuracy being 91.3%. The robustness of the trained YSODA is demonstrated and verified in detecting small-sized defects. It is also compared with that of the traditional CNN-based and machine learning methods by applying to a real WTB system, which proved that the proposed YSODA is superior to existing approaches in terms of detection accuracy and reliability.

Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics

Image processing involved specifically with image recognition and image classification has taken a huge stride with the advent of high-performance GPU’s and the increase in the processing speeds of images by the computer systems. Also, the availability of huge datasets on different classes of images has helped in solving issues which can be addressed by huge number of annotated datasets in teaching a supervised Convolutional Neural Network (CNN) model to classify the images. The present method of Structural Health Monitoring (SHM) of Wind Turbine Blade (WTB) inspection manually is involved with a great amount of risk and also takes a lot of time for inspection of each turbine and the operation time of each turbine comes down as the turbine should be at halt while inspecting. These problems are overcome by the inspection through drones and the basic idea of this research is to use deep learning with keras frame work written in python working on top of tensor flow to use the drone captured images to train a neural network model and classify into faulty and not faulty images of blades, which when deployed can be used in classifying new images. This method in turn reduces the maintenance time and inspection time and less risk for the inspection of WTB and in turn the SHM. In this research CNN’s are used to find whether the given image of WTB is having damage or not. Since the image data of wind turbine blades are not available easily with annotated images as like ImageNet and AlexNet, this project was developed from scratch right from acquiring the image data. Along the way, the focus was on the influence of certain hyper-parameters and on seeking theoretically founded ways to adapt them, all with the objective of progressing to satisfactory results as fast as possible. In the end, also a promising attempt in classifying WTB damages into a few different classes is presented and deploying the saved model using Flask such that the model can be used anywhere when required. Accuracies of around 94.94% for binary fault classification and 91% accuracy for multiple class fault classification has been achieved. The significance of the proposed method is that it is the first of its kind for WTB damage classification and detection for different classes of damage without using transfer learning but by training the model from the image dataset prepared by image augmentation methods and manual editing.

Risk-based approach for rational categorization of damage observations from wind turbine blade inspections

This study provides a risk-based assessment procedure for wind turbine blade damages observed during visual inspections. A decision model is presented which identifies the cost-optimal intervention based on assessed damage severity. This is achieved by defining procedures for model-based estimation of probability of consequences for specific failure modes, and by analysing the costs associated with different scenarios for intervention. In addition, the procedure provides a risk-based, quantitative interpretation of damage severity categories used in wind turbine blade inspection practices. In the present paper, the workflow and example categorization are demonstrated on two specific faults in wind turbine blades: leading edge erosion damage, and trailing edge crack.

Comparison of nondestructive testing techniques for the inspection of wind turbine blades' spar caps

AbstractTo increase the energy conversion efficiency and power production, the size of wind turbine blades has continued to grow larger. As such, the need to identify defects and monitor the structural health becomes more critical to maintain reliability and prevent costly catastrophic failures. Larger blades are subject to an increased likelihood of operational damage and manufacturing defects. Usually, glass‐reinforced spar caps are inspected with the assistance of strong flashlights, while nondestructive testing (NDT) inspections are performed only on carbon‐reinforced blades which are not translucent. These operations provide qualitative information only. Therefore, there is a growing need for inspection techniques that can provide quantitative information for assessing the structural health of the blades. In recent years, different NDT techniques capable of accurately delivering surface and internal data have been developed. Nondestructive testing systems should be able to quickly and efficiently scan large areas, to be economically beneficial to the wind turbine manufacturing industry. Within this paper, 3 different NDT techniques for the inspection of manufactured wind turbine blades' spar caps are compared: terahertz inverse synthetic aperture radar, infrared thermography, and X‐ray imaging. Each is investigated to evaluate their ability to detect the presence of defects of concern that are created during the blade manufacturing process. Through a set of experimental tests, the advantages, challenges, and drawbacks of each used technique are evaluated and compared in the context of the needs of the wind turbine industry. This research provides the foundation of empirical comparisons that can lead to the development of more accurate NDT resulting in the construction of more reliable and less‐expensive wind turbine blades.

Effects of defects in composite wind turbine blades – Part 1: Characterization and mechanical testing

Abstract. The Montana State University Composite Material Technologies Research Group performed a study to ascertain the effects of defects that often result from the manufacture of composite wind turbine blades. The first step in this multiyear study was to systematically quantify and enter these defects into a database before embedding similar defects into manufactured coupons. Through the Sandia National Laboratories Blade Reliability Collaborative (BRC), it was determined that key defects to investigate were fiber waves and porosity. An inspection of failed commercial-scale wind turbine blades yielded metrics that utilized specific parameters to physically characterize a defect. Methods to easily and consistently discretize, measure, and assess these defects based on the identified parameters were established to allow for statistical analysis. Data relating flaw parameters to frequencies of occurrence were analyzed and found to fit within standard distributions. Additionally, mechanical testing of coupons with flaws based on these physical characterization data was performed to understand effects of these defects. Representative blade materials and manufacturing methods were utilized and both material properties and damage progression were measured. It was observed that flaw parameters directly affected the mechanical response. While the data gathered in this first step are widely useful, it was also intended for use as a foundation for the rest of the study, to perform probabilistic analysis and comparative analysis of progressive damage models.

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.