Research on fault diagnosis of transmission line based on SIFT feature

Authors:
Shujia Yan;Lijun Jin;Zhe Zhang;Wenhao Zhang
Affiliations:
College of Electronic and Information Engineering, Tongji University, Shanghai, China;College of Electronic and Information Engineering, Tongji University, Shanghai, China;Tongyuan Architectural Design Institute, Tongji Architectural Design (GROUP) CO., LTD, Shanghai, China;College of Electronic and Information Engineering, Tongji University, Shanghai, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

Recent interest in line-tracking methods using UAV has been introduced in the research of pattern recognition and diagnosis of transmission system. A fault diagnosis method for transmission line based on Scale Invariant Feature Transform (SIFT) is proposed in this paper, which recognizes fault images by comparing aerial images with model images. The reliability and efficiency of the system is effectively improved by pro-calculating local scale-invariant features of models. The research can provide a new method for predictive maintenance of the transmission line.