Feasibility of Automatic Detection of Surface Cracks in Wind Turbine Blades

Zhang, Huiyi; Jackman, John

doi:10.1260/0309-524x.38.6.575

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…With the advancement in wind-power generation technology, many studies have been performed with an objective to replace human-based inspection techniques with an automatic system to detect and identify blade defects. Reference [9] proposed an optical inspection method for the detection of surface cracks on an installed blade and demonstrated crack detection by using the Sobel and Canny image-processing technique. One of the most recent trends in defect detection has involved automatic classification of inspection results by using machine learning (ML) and neural networks (NN).…”

Section: B Classification Methodsmentioning

confidence: 99%

Automatic Discontinuity Classification of Wind-turbine Blades Using A-scan-based Convolutional Neural Network

Choung

Lim

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Recent development trends in wind power generation have increased the importance of the safe operation of wind-turbine blades (WTBs). To realize this objective, it is essential to inspect WTBs for any defects before they are placed into operation. However, conventional methods of fault inspection in WTBs can be rather difficult to implement, since complex curvatures that characterize the WTB structures must ensure accurate and reliable inspection. Moreover, it is considered useful if inspection results can be objectively and consistently classified and analyzed by an automated system and not by the subjective judgment of an inspector. To address this concern, the construction of a pressure-and shape-adaptive phased-array ultrasonic testing platform, which is controlled by a nanoengine operation system to inspect WTBs for internal defects, has been presented in this paper. An automatic classifier has been designed to detect discontinuities in WTBs by using an A-scanimaging-based convolutional neural network (CNN). The proposed CNN classifier design demonstrates a classification accuracy of nearly 99%. Results of the study demonstrate that the proposed CNN classifier is capable of automatically classifying the discontinuities of WTB with high accuracy, all of which could be considered as defect candidates.

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Section: B Classification Methodsmentioning

confidence: 99%

Automatic Discontinuity Classification of Wind-turbine Blades Using A-scan-based Convolutional Neural Network

Choung

Lim

et al. 2021

Journal of Modern Power Systems and Clean Energy

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“…[86] and Ref. [87]. In this sense, the Canny algorithm was found to be the most efficient choice in many similar studies [88,89].…”

Section: Traditional Enhanced and Automatic Visual Inspection (Vi)mentioning

confidence: 98%

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

Civera

Surace

2022

Sensors

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A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.

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“…The tunneling crack tool can be extended to account for delamination during the tunneling process [67,25,19] or expanded to handle gel coat channeling cracks in wind turbine blade surfaces during cyclic loading [68].…”

Section: Proposed Extensions Of the Present Workmentioning

confidence: 99%

Tunneling cracks in full scale wind turbine blade joints

Jørgensen

Sørensen

Kildegaard³

2018

Engineering Fracture Mechanics

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A novel approach is presented and used in a generic tunneling crack tool for the prediction of crack growth rates for tunneling cracks propagating across a bond-line in a wind turbine blade under high cyclic loadings. In order to test and demonstrate the applicability of the tool, model predictions are compared with measured crack growth rates from a full scale blade fatigue test. The crack growth rates, measured for a several metre long section along the blade trailing-edge joint during the fatigue test, are found to be in-between the upper-and lower-bound predictions.

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Feasibility of Automatic Detection of Surface Cracks in Wind Turbine Blades

Cited by 11 publications

References 8 publications

Automatic Discontinuity Classification of Wind-turbine Blades Using A-scan-based Convolutional Neural Network

Automatic Discontinuity Classification of Wind-turbine Blades Using A-scan-based Convolutional Neural Network

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

Tunneling cracks in full scale wind turbine blade joints

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