Suzhou Electric Appliance Research Institute
期刊號： CN32-1800/TM| ISSN1007-3175

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用于電力設(shè)備異常診斷的圖像配準(zhǔn)及融合方法

來源：電工電氣發(fā)布時間：2024-12-02 08:02 瀏覽次數(shù)：44

用于電力設(shè)備異常診斷的圖像配準(zhǔn)及融合方法

周喜紅1，席亞賓1，李中寶2
(1 廣東粵電大亞灣綜合能源有限公司，廣東 惠州 516000；
2 中國核工業(yè)二三建設(shè)有限公司，北京 101300)
 
    摘 要：近年來圖像融合方法在電力設(shè)備熱異常的診斷中所占比重逐漸增加，但是涉及到圖像配準(zhǔn)和融合統(tǒng)一考慮的方法很少。提出了一種最大迭代關(guān)聯(lián)圖像配準(zhǔn)及區(qū)域特性判別的圖像融合方法，用于輔助熱異常的診斷。該方法通過構(gòu)建約束函數(shù)計算源圖像配準(zhǔn)迭代次數(shù)，隸屬度函數(shù)定義源圖像的區(qū)域特性，已知區(qū)域特性的子圖像根據(jù)電力設(shè)備熱異常所重視的特征優(yōu)先選擇融合策略，以最大程度保留源圖像中的紋理特征和熱輻射特征。在自建的電力設(shè)備數(shù)據(jù)集上與其他方法對比顯示，所提方法在保證源圖像配準(zhǔn)精度的前提下，還突出了紅外圖像的熱輻射特征和可見光圖像的紋理特征，能夠滿足電力設(shè)備熱異常診斷的需要。
    關(guān)鍵詞： 圖像融合；圖像配準(zhǔn)；電力設(shè)備；熱異常診斷；約束函數(shù)；隸屬度函數(shù)；熱輻射
    中圖分類號：TM711 ；TP391     文獻(xiàn)標(biāo)識碼：B     文章編號：1007-3175(2024)11-0067-10
 
Image Registration and Fusion Method for Anomaly
Diagnosis of Power Equipment
 
ZHOU Xi-hong1, XI Ya-bin1, LI Zhong-bao2
（1 Guangdong Yuedian Daya Bay Integrated Energy Co., Ltd, Huizhou 516000, China;
2 China Nuclear Industry 23 Construction Co., Ltd, Beijing 101300, China）
 
    Abstract: In recent years, the proportion of image fusion methods in the diagnosis of thermal anomalies of power equipment has gradually increased, but the methods involving unified consideration of image registration and fusion are rare. Therefore, this paper proposes an image fusion method based on maximum iterative correlation image registration and regional feature discrimination, which is used to assist thermal anomaly diagnosis. This method calculates the number of source image registration iterations by constructing a constraint function, and the membership function defines the regional characteristics of the source image. The sub-images with known regional characteristics preferentially select the fusion strategy according to the characteristics that the thermal anomaly of the power equipment attaches importance to, so as to retain the texture features and thermal radiation features in the source image to the greatest extent. Compared with other methods on the self-built power equipment dataset, the proposed method not only ensures the registration accuracy of the source image, but also highlights the thermal radiation characteristics of the infrared image and the texture characteristics of the visible image, which can meet the needs of thermal anomaly diagnosis of power equipment.
    Key words: image fusion; image registration; power equipment; thermal anomaly diagnosis; constraint function; membership function;thermal radiation
 
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