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

Reddy, A. Pramod; Indragandhi, V.; Ravi, Logesh; Subramaniyaswamy, V.

doi:10.1016/j.measurement.2019.07.051

Cited by 118 publications

(45 citation statements)

References 14 publications

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“…The researchers used faster R-CNN and achieved a mean average precision of 81.10% on four types of damage. Similarly, Reddy et al [10] used convolutional neural networks to classify and detect various types of damage on the wind turbine blade. The accuracy achieved was 94.49% for binary classification and 90.6% for multi class classification.…”

Section: Applications/breakthroughs Of Computer Visionmentioning

confidence: 99%

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Doğru

Bouarfa²,

Arizar³

et al. 2020

Aerospace

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Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%).

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Section: Applications/breakthroughs Of Computer Visionmentioning

confidence: 99%

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Doğru

Bouarfa²,

Arizar³

et al. 2020

Aerospace

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show abstract

“…In more recent works, the concept of a modern DT was outlined for application to aircraft and aerospace structures [4,5], and further reviews on the application of DT technology can be found in [6,7]. Studies [8][9][10][11][12][13] have discussed the development of a digital twin of wind turbines, structural health monitoring using Supervisory Control And Data Acquisition (SCADA) data and autonomous Unmanned Aerial Vehicles (UAVs), damage detection using an AI-assisted image process, and alternative algorithms for wind turbines. These studies [8][9][10][11][12][13] have contributed to bringing more autonomy, automation, and optimization into wind turbines, which increasingly grow in size and complexity.…”

Section: Introduction To the Bigger Picturementioning

confidence: 99%

A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance

et al. 2021

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This work proposes a novel concept for an intelligent and semi-autonomous human-cyber-physical system (HCPS) to operate future wind turbines in the context of Industry 5.0 technologies. The exponential increase in the complexity of next-generation wind turbines requires artificial intelligence (AI) to operate the machines efficiently and consistently. Evolving the current Industry 4.0 digital twin technology beyond a sole aid for the human decision-making process, the digital twin in the proposed system is used for highly effective training of the AI through machine learning. Human intelligence (HI) is elevated to a supervisory level, in which high-level decisions made through a human–machine interface break the autonomy, when needed. This paper also identifies and elaborates key enabling technologies (KETs) that are essential for realizing the proposed HCPS.

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“…For faster and more reliable asset management, automatic crack detection systems have been developed to minimise the subjectivity of traditional human inspection procedures, in particular with the involvement of unmanned aerial vehicles (UAVs). 9,10 A number of approaches have been proposed to address the challenge of manual inspection using CVbased crack detection. From the aspect of manual inputs, existing approaches can be divided into rulebased and machine learning-based (ML-based) methods.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Imaging-based crack detection on concrete surfaces using You Only Look Once network

Deng

Lee

2020

Structural Health Monitoring

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The detection of cracks in concrete structures is a pivotal aspect in assessing structural robustness. Current inspection methods are subjective, relying on the inspector’s experience and mental focus. In this study, an ad hoc You Only Look Once version 2 object detector was applied to automatically detect concrete cracks from real-world images, which were taken from diverse concrete bridges and contaminated with handwriting scripts. A total of 3010 cropped images were used to generate the dataset, labelled for two different detection classes, that is, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Three full-scale images that contained disturbing background information were used to evaluate the robustness of the trained detector. The influence of labelling handwriting as an object class for network training on the overall crack detection accuracy was assessed as well. The results of this study show that the You Only Look Once version 2 could automatically locate crack with bounding boxes from raw images, even with the presence of handwriting scripts. As a comparative study, the proposed network was also compared with faster region-based convolutional neural network. The results showed that You Only Look Once version 2 performed better in terms of both accuracy and inference speed.

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

Cited by 118 publications

References 14 publications

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance

Imaging-based crack detection on concrete surfaces using You Only Look Once network

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