Power Metal Corrosion Evaluation Method Based on Image Feature Analysis
In order to quickly and effectively diagnose the metal corrosion state of power equipment，the corrosion image of the metal material Q235 steel in the hanging piece experiment of the substation was analyzed，and a metal corrosion state evaluation method based on the analysis of the corrosion image cha...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2021-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1924 |
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| author | ZHONG Yao ＲEN Xiao WU Gao-lin WANG Qian WANG Xu-peng HAO Jian |
| author_facet | ZHONG Yao ＲEN Xiao WU Gao-lin WANG Qian WANG Xu-peng HAO Jian |
| author_sort | ZHONG Yao |
| collection | DOAJ |
| description | In order to quickly and effectively diagnose the metal corrosion state of power equipment，the corrosion image of the metal material Q235 steel in the hanging piece experiment of the substation was analyzed，and a metal corrosion state evaluation method based on the analysis of the corrosion image characteristics was proposed. First，multi-dimensional feature parameters were extracted through image preprocessing，chromatics，statistics， wavelet and fractal analysis methods; then，a metal corrosion state evaluation method was proposed based on neural network algorithm，and the effectiveness of the method was verified. The results show that the color，statistics， wavelet and fractal characteristic parameters of the corrosion image can fully reflect the evolution law and corrosion state of the metal corrosion morphology，and the corrosion evaluation model constructed by the neural networkalgorithm and the multi-dimensional characteristic parameters can accurately evaluate the metal corrosion degree. The evaluation results of the corrosion status of different corrosion samples on site are consistent with their actual corrosion degree. |
| format | Article |
| id | doaj-art-4ec4e7afd245445f98843ce3bc500b40 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2021-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-4ec4e7afd245445f98843ce3bc500b402025-08-20T04:02:22ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832021-02-01260110010810.15938/j.jhust.2021.01.014Power Metal Corrosion Evaluation Method Based on Image Feature AnalysisZHONG Yao0ＲEN Xiao1WU Gao-lin2WANG Qian3WANG Xu-peng4HAO Jian5State Key Laboratory of Transmission and Distribution Equipment and Power System Safety and New Technology，Chongqing University，Chongqing 400044，ChinaElectric Power Ｒesearch Institute，State Grid Chongqing Electric Power Company，Chongqing 401123，ChinaElectric Power Ｒesearch Institute，State Grid Chongqing Electric Power Company，Chongqing 401123，ChinaElectric Power Ｒesearch Institute，State Grid Chongqing Electric Power Company，Chongqing 401123，ChinaState Key Laboratory of Transmission and Distribution Equipment and Power System Safety and New Technology，Chongqing University，Chongqing 400044，ChinaState Key Laboratory of Transmission and Distribution Equipment and Power System Safety and New Technology，Chongqing University，Chongqing 400044，ChinaIn order to quickly and effectively diagnose the metal corrosion state of power equipment，the corrosion image of the metal material Q235 steel in the hanging piece experiment of the substation was analyzed，and a metal corrosion state evaluation method based on the analysis of the corrosion image characteristics was proposed. First，multi-dimensional feature parameters were extracted through image preprocessing，chromatics，statistics， wavelet and fractal analysis methods; then，a metal corrosion state evaluation method was proposed based on neural network algorithm，and the effectiveness of the method was verified. The results show that the color，statistics， wavelet and fractal characteristic parameters of the corrosion image can fully reflect the evolution law and corrosion state of the metal corrosion morphology，and the corrosion evaluation model constructed by the neural networkalgorithm and the multi-dimensional characteristic parameters can accurately evaluate the metal corrosion degree. The evaluation results of the corrosion status of different corrosion samples on site are consistent with their actual corrosion degree.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1924corrosion imagemultidimensional featureevaluation systemcorrosion assessm |
| spellingShingle | ZHONG Yao ＲEN Xiao WU Gao-lin WANG Qian WANG Xu-peng HAO Jian Power Metal Corrosion Evaluation Method Based on Image Feature Analysis Journal of Harbin University of Science and Technology corrosion image multidimensional feature evaluation system corrosion assessm |
| title | Power Metal Corrosion Evaluation Method Based on Image Feature Analysis |
| title_full | Power Metal Corrosion Evaluation Method Based on Image Feature Analysis |
| title_fullStr | Power Metal Corrosion Evaluation Method Based on Image Feature Analysis |
| title_full_unstemmed | Power Metal Corrosion Evaluation Method Based on Image Feature Analysis |
| title_short | Power Metal Corrosion Evaluation Method Based on Image Feature Analysis |
| title_sort | power metal corrosion evaluation method based on image feature analysis |
| topic | corrosion image multidimensional feature evaluation system corrosion assessm |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1924 |
| work_keys_str_mv | AT zhongyao powermetalcorrosionevaluationmethodbasedonimagefeatureanalysis AT renxiao powermetalcorrosionevaluationmethodbasedonimagefeatureanalysis AT wugaolin powermetalcorrosionevaluationmethodbasedonimagefeatureanalysis AT wangqian powermetalcorrosionevaluationmethodbasedonimagefeatureanalysis AT wangxupeng powermetalcorrosionevaluationmethodbasedonimagefeatureanalysis AT haojian powermetalcorrosionevaluationmethodbasedonimagefeatureanalysis |