Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment  

作　　者：Guanghua Zheng Chaolin Luo Mengen Shen Wanzhong Lv Wenbo Jiang Weibo Yang 

机构地区：[1]Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment,School of Mechatronic Engineering,China University of Mining and Technology,Xuzhou,22116,China [2]Anji Chang Hong Chain Manufacturer Co.,Ltd.,Anji,313300,China [3]Pearl RiverWater Resources Research Institute,PRHRI,Guangzhou,510000,China [4]Henan Pinggao Electric Co.,Ltd.,Pinggao Group Co.,Ltd.,Pingdingshan,467001,China

出　　处：《Energy Engineering》2023年第4期985-1000,共16页能源工程（英文）

摘　　要：To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an automatic feature extraction algorithm is proposed.Firstly,the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method.Then,binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation.Finally,the automatic identification of weld defect area is realized based on the sequential traversal of binary tree.Several characteristic analysis dimensions are established for weld defects of UHV key parts,including defect area,perimeter,slenderness ratio,duty cycle,etc.The experiment using theweld detection image of the actual production site shows that the proposedmethod can effectively extract theweak feature information ofweld defects and further provide reference for decision-making.

关 键 词：UHV equipment WELD nondestructive testing weak feature extraction 

分 类 号：TM723[电气工程—电力系统及自动化]