Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art
Abstract As a non‐contact temperature distribution measurement method, infrared thermography (IRT) has emerged as an indispensable tool in condition monitoring and fault diagnosis of electrical equipment based on the absolute and relative temperature values. Manual fault inspection, as an expert‐exp...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | High Voltage |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/hve2.12023 |
| _version_ | 1798018268890398720 |
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| author | Changjie Xia Ming Ren Bing Wang Ming Dong Guanghao Xu Jiacheng Xie Chongxing Zhang |
| author_facet | Changjie Xia Ming Ren Bing Wang Ming Dong Guanghao Xu Jiacheng Xie Chongxing Zhang |
| author_sort | Changjie Xia |
| collection | DOAJ |
| description | Abstract As a non‐contact temperature distribution measurement method, infrared thermography (IRT) has emerged as an indispensable tool in condition monitoring and fault diagnosis of electrical equipment based on the absolute and relative temperature values. Manual fault inspection, as an expert‐experiences based evaluation method, has formed a mature technical scheme with a large number of application cases. However, the efficiency and accuracy of manual fault inspection are being challenged by the rapid growth in the number of equipment in power grid. The situation is improving with the advanced of image processing technique. Machine‐assisted fault diagnosis provides a novel method to assist human beings to complete fault diagnosis under the intervention of human prior knowledge. However, the limitations of infrared images bring challenges to image analysis processing especially target detection. In pursuit of automatic fault diagnosis, deep learning algorithms are introduced to achieve target detection in the complex environment. This study reviews the development of IRT‐based diagnostics beginning with the general procedures, objects, and limitations of IRT‐based fault inspection, and then gives an insight into the popular machine‐assisted fault diagnosis as well as image‐based intelligent fault identification. In addition, the future recommendations of IRT are also provided from construction of intelligent infrared detection system, establishment of an open and shared infrared image database and comprehensive utilization of joint visualization diagnosis technology. |
| first_indexed | 2024-04-11T16:21:07Z |
| format | Article |
| id | doaj.art-8711ff4030954d4a84fcdea5a0abcbd9 |
| institution | Directory Open Access Journal |
| issn | 2397-7264 |
| language | English |
| last_indexed | 2024-04-11T16:21:07Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | High Voltage |
| spelling | doaj.art-8711ff4030954d4a84fcdea5a0abcbd92022-12-22T04:14:21ZengWileyHigh Voltage2397-72642021-06-016338740710.1049/hve2.12023Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐artChangjie Xia0Ming Ren1Bing Wang2Ming Dong3Guanghao Xu4Jiacheng Xie5Chongxing Zhang6State Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'an ChinaState Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'an ChinaState Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'an ChinaState Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'an ChinaState Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'an ChinaState Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'an ChinaState Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'an ChinaAbstract As a non‐contact temperature distribution measurement method, infrared thermography (IRT) has emerged as an indispensable tool in condition monitoring and fault diagnosis of electrical equipment based on the absolute and relative temperature values. Manual fault inspection, as an expert‐experiences based evaluation method, has formed a mature technical scheme with a large number of application cases. However, the efficiency and accuracy of manual fault inspection are being challenged by the rapid growth in the number of equipment in power grid. The situation is improving with the advanced of image processing technique. Machine‐assisted fault diagnosis provides a novel method to assist human beings to complete fault diagnosis under the intervention of human prior knowledge. However, the limitations of infrared images bring challenges to image analysis processing especially target detection. In pursuit of automatic fault diagnosis, deep learning algorithms are introduced to achieve target detection in the complex environment. This study reviews the development of IRT‐based diagnostics beginning with the general procedures, objects, and limitations of IRT‐based fault inspection, and then gives an insight into the popular machine‐assisted fault diagnosis as well as image‐based intelligent fault identification. In addition, the future recommendations of IRT are also provided from construction of intelligent infrared detection system, establishment of an open and shared infrared image database and comprehensive utilization of joint visualization diagnosis technology.https://doi.org/10.1049/hve2.12023condition monitoringfault diagnosisinfrared imaginginspectionlearning (artificial intelligence)object detection |
| spellingShingle | Changjie Xia Ming Ren Bing Wang Ming Dong Guanghao Xu Jiacheng Xie Chongxing Zhang Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art High Voltage condition monitoring fault diagnosis infrared imaging inspection learning (artificial intelligence) object detection |
| title | Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art |
| title_full | Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art |
| title_fullStr | Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art |
| title_full_unstemmed | Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art |
| title_short | Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art |
| title_sort | infrared thermography based diagnostics on power equipment state of the art |
| topic | condition monitoring fault diagnosis infrared imaging inspection learning (artificial intelligence) object detection |
| url | https://doi.org/10.1049/hve2.12023 |
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