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基于無(wú)人機的風(fēng)機葉片表面缺陷自動(dòng)檢測方法
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大唐山西新能源公司

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Automatic detection method for surface defects of fan blades based on drones
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    摘要:

    風(fēng)機葉片是風(fēng)力發(fā)電系統的核心部件,受到氣候條件、工作負荷等因素的影響,容易出現各類(lèi)缺陷,如裂紋、磨損、腐蝕等。如果不能及時(shí)發(fā)現和解決這些缺陷,將導致風(fēng)機性能下降、損壞甚至引發(fā)事故。為此,研究一種基于無(wú)人機的風(fēng)機葉片表面缺陷自動(dòng)檢測方法。利用無(wú)人機搭載攝像機,飛到高空當中,拍攝空中運行的葉片圖像。對葉片圖像實(shí)施灰度化、去噪以及照度均衡化處理,提升圖像質(zhì)量。提取葉片圖像中的幾何特征和紋理特征。利用差異演化算法改進(jìn)概率神經(jīng)網(wǎng)絡(luò )平滑參數,以?xún)?yōu)化后的概率神經(jīng)網(wǎng)絡(luò )為基礎構建分類(lèi)識別模型,將幾何特征和紋理特征作為輸入,計算每種類(lèi)別的輸出概率,將最大值響應原則將概率數值最大的類(lèi)別作為判定的缺陷類(lèi)別,以此實(shí)現風(fēng)機葉片表面缺陷自動(dòng)檢測。結果表明:所研究技術(shù)應用下,杰卡德系數相對更高,說(shuō)明該方法的檢測結果更為準確;所花費時(shí)間相對更少,說(shuō)明該方法的檢測效率更高,可以更快地完成檢測任務(wù)。

    Abstract:

    Wind turbine blades are the core components of wind power generation systems, which are susceptible to various defects such as cracks, wear, and corrosion due to factors such as climate conditions and working loads. If these defects cannot be detected and resolved in a timely manner, it will lead to a decrease in fan performance, damage, and even accidents. To this end, a method for automatic detection of surface defects on fan blades based on drones is studied. Using a drone equipped with a camera, fly high into the air to capture images of the blades moving in the air. Implement grayscale, denoising, and illumination equalization processing on leaf images to improve image quality. Extract geometric and texture features from leaf images. Using differential evolution algorithm to improve the smoothing parameters of probability neural network, constructing a classification recognition model based on the optimized probability neural network, taking geometric and texture features as inputs, calculating the output probability of each category, and using the maximum response principle to determine the defect category with the highest probability value, in order to achieve automatic detection of surface defects on wind turbine blades. The results show that under the application of the studied technology, the Jaccard coefficient is relatively higher, indicating that the detection results of this method are more accurate; The relatively less time spent indicates that this method has higher detection efficiency and can complete detection tasks faster.

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閆浩偉.基于無(wú)人機的風(fēng)機葉片表面缺陷自動(dòng)檢測方法計算機測量與控制[J].,2024,32(11):72-79.

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  • 收稿日期:2024-02-19
  • 最后修改日期:2024-03-21
  • 錄用日期:2024-03-22
  • 在線(xiàn)發(fā)布日期: 2024-11-19
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