Visual inspection for transformer insulation defects by a patrol robot fish based on deep learning
Abstract In order to effectively detect the internal insulation defects of large transformers, a miniature patrol robot fish is designed and used to observe the pressboard inside the transformer, which can visually inspect the insulation condition of the pressboard. In the process of visual inspecti...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-09-01
|
Series: | IET Science, Measurement & Technology |
Online Access: | https://doi.org/10.1049/smt2.12062 |
id |
doaj-217c9bc1d48f487a948e4426cb4ee711 |
---|---|
record_format |
Article |
spelling |
doaj-217c9bc1d48f487a948e4426cb4ee7112021-08-06T06:18:12ZengWileyIET Science, Measurement & Technology1751-88221751-88302021-09-0115760661810.1049/smt2.12062Visual inspection for transformer insulation defects by a patrol robot fish based on deep learningHongxin Ji0Xiwang Cui1Weiyan Ren2Liqing Liu3Wei Wang4School of Aerospace Engineering Tsinghua University Beijing ChinaSchool of Instrument Science and Opto‐electronics Engineering Beijing Information Science and Technology University Beijing ChinaSchool of Aerospace Engineering Tsinghua University Beijing ChinaState Grid Tianjin Electric Power Research Institute Tianjin ChinaState Grid Tianjin Electric Power Research Institute Tianjin ChinaAbstract In order to effectively detect the internal insulation defects of large transformers, a miniature patrol robot fish is designed and used to observe the pressboard inside the transformer, which can visually inspect the insulation condition of the pressboard. In the process of visual inspection by a patrol robot fish, insulation defects observed in the transformer (such as discharge carbon marks, pressboard cracks, etc.) are small size, indistinctive colour contrast and different shapes. The key purpose of the patrol robot fish is to identify the defect targets and defect types intelligently and quickly from the images taken by the camera on the fish. Considering that there are abundant insulation defects in transformers and a lack of samples for learning, a vision detection method based on deep learning network is proposed in this study. The proposed method integrates the variable autoencoder into the traditional Faster‐RCNN target detection network and constructs an improved Faster‐RCNN that enhances feature extraction. This method expands the small‐scale training sample set and improves the generalisation performance of the model. In order to verify the effectiveness of the proposed method, the improved network is trained and tested, and the test results show that the improved network training model can accurately identify and mark the carbon marks on the pressboard surface.https://doi.org/10.1049/smt2.12062 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongxin Ji Xiwang Cui Weiyan Ren Liqing Liu Wei Wang |
spellingShingle |
Hongxin Ji Xiwang Cui Weiyan Ren Liqing Liu Wei Wang Visual inspection for transformer insulation defects by a patrol robot fish based on deep learning IET Science, Measurement & Technology |
author_facet |
Hongxin Ji Xiwang Cui Weiyan Ren Liqing Liu Wei Wang |
author_sort |
Hongxin Ji |
title |
Visual inspection for transformer insulation defects by a patrol robot fish based on deep learning |
title_short |
Visual inspection for transformer insulation defects by a patrol robot fish based on deep learning |
title_full |
Visual inspection for transformer insulation defects by a patrol robot fish based on deep learning |
title_fullStr |
Visual inspection for transformer insulation defects by a patrol robot fish based on deep learning |
title_full_unstemmed |
Visual inspection for transformer insulation defects by a patrol robot fish based on deep learning |
title_sort |
visual inspection for transformer insulation defects by a patrol robot fish based on deep learning |
publisher |
Wiley |
series |
IET Science, Measurement & Technology |
issn |
1751-8822 1751-8830 |
publishDate |
2021-09-01 |
description |
Abstract In order to effectively detect the internal insulation defects of large transformers, a miniature patrol robot fish is designed and used to observe the pressboard inside the transformer, which can visually inspect the insulation condition of the pressboard. In the process of visual inspection by a patrol robot fish, insulation defects observed in the transformer (such as discharge carbon marks, pressboard cracks, etc.) are small size, indistinctive colour contrast and different shapes. The key purpose of the patrol robot fish is to identify the defect targets and defect types intelligently and quickly from the images taken by the camera on the fish. Considering that there are abundant insulation defects in transformers and a lack of samples for learning, a vision detection method based on deep learning network is proposed in this study. The proposed method integrates the variable autoencoder into the traditional Faster‐RCNN target detection network and constructs an improved Faster‐RCNN that enhances feature extraction. This method expands the small‐scale training sample set and improves the generalisation performance of the model. In order to verify the effectiveness of the proposed method, the improved network is trained and tested, and the test results show that the improved network training model can accurately identify and mark the carbon marks on the pressboard surface. |
url |
https://doi.org/10.1049/smt2.12062 |
work_keys_str_mv |
AT hongxinji visualinspectionfortransformerinsulationdefectsbyapatrolrobotfishbasedondeeplearning AT xiwangcui visualinspectionfortransformerinsulationdefectsbyapatrolrobotfishbasedondeeplearning AT weiyanren visualinspectionfortransformerinsulationdefectsbyapatrolrobotfishbasedondeeplearning AT liqingliu visualinspectionfortransformerinsulationdefectsbyapatrolrobotfishbasedondeeplearning AT weiwang visualinspectionfortransformerinsulationdefectsbyapatrolrobotfishbasedondeeplearning |
_version_ |
1721219315183648768 |