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

Guanghua Zheng1,2, Chaolin Luo1,3, Mengen Shen1,*, Wanzhong Lv4, Wenbo Jiang4, Weibo Yang2

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 River Water Resources Research Institute, PRHRI, Guangzhou, 510000, China
4 Henan Pinggao Electric Co., Ltd., Pinggao Group Co., Ltd., Pingdingshan, 467001, China

* Corresponding Author: Mengen Shen. Email: email

(This article belongs to this Special Issue: Dynamics and Fault Diagnosis for Energy Equipment)

Energy Engineering 2023, 120(4), 985-1000.


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 the weld detection image of the actual production site shows that the proposed method can effectively extract the weak feature information of weld defects and further provide reference for decision-making.


Cite This Article

Zheng, G., Luo, C., Shen, M., Lv, W., Jiang, W. et al. (2023). Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment. Energy Engineering, 120(4), 985–1000.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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