Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories
Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to real...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/14/4316 |
| _version_ | 1827687571759562752 |
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| author | Lixiao Mu Xiaobing Xu Zhanran Xia Bin Yang Haoran Guo Wenjun Zhou Chengke Zhou |
| author_facet | Lixiao Mu Xiaobing Xu Zhanran Xia Bin Yang Haoran Guo Wenjun Zhou Chengke Zhou |
| author_sort | Lixiao Mu |
| collection | DOAJ |
| description | Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper. |
| first_indexed | 2024-03-10T09:40:04Z |
| format | Article |
| id | doaj.art-45568a9be9cb417882db2be8b29fbf10 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T09:40:04Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-45568a9be9cb417882db2be8b29fbf102023-11-22T03:43:38ZengMDPI AGEnergies1996-10732021-07-011414431610.3390/en14144316Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable AccessoriesLixiao Mu0Xiaobing Xu1Zhanran Xia2Bin Yang3Haoran Guo4Wenjun Zhou5Chengke Zhou6State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaState Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, ChinaState Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, ChinaState Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaInfrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.https://www.mdpi.com/1996-1073/14/14/4316cable accessoriesinfrared image processingFaster RCNNMean-Shift algorithmsmart condition diagnosis |
| spellingShingle | Lixiao Mu Xiaobing Xu Zhanran Xia Bin Yang Haoran Guo Wenjun Zhou Chengke Zhou Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories Energies cable accessories infrared image processing Faster RCNN Mean-Shift algorithm smart condition diagnosis |
| title | Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories |
| title_full | Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories |
| title_fullStr | Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories |
| title_full_unstemmed | Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories |
| title_short | Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories |
| title_sort | autonomous analysis of infrared images for condition diagnosis of hv cable accessories |
| topic | cable accessories infrared image processing Faster RCNN Mean-Shift algorithm smart condition diagnosis |
| url | https://www.mdpi.com/1996-1073/14/14/4316 |
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