Development of a Pendulum-Type Power Transmission Line Inspection Robot and Anomaly Detection Methods of Power Transmission Line
-
- Hayashi Fumihiro
- Graduate School of Sciences and Technology for Innovation,Tokushima University Department of Electronic Systems Engineering, National Institute of Technology (KOSEN), Kagawa College Mitoyo AI Development Co. Ltd.
-
- Miwa Masafumi
- Graduate School of Sciences and Technology for Innovation,Tokushima University
-
- Misaki Yukinori
- Department of Electronic Systems Engineering, National Institute of Technology (KOSEN), Kagawa College
-
- Iwamoto Naoya
- Department of Electronic Systems Engineering, National Institute of Technology (KOSEN), Kagawa College
-
- Takechi Taiga
- Mitoyo AI Development Co. Ltd.
Bibliographic Information
- Other Title
-
- 重心移動型フレームによる送電線点検ロボットおよび送電線の異常検出手法の開発
- ジュウシン イドウガタ フレーム ニ ヨル ソウデンセン テンケン ロボット オヨビ ソウデンセン ノ イジョウ ケンシュツ シュホウ ノ カイハツ
Search this article
Abstract
<p>Power transmission lines are vital infrastructures in our daily lives. We have developed a remote-controlled robot that can run on the transmission lines and capture clear images to improve the safety, accuracy, and efficiency of power transmission line inspections. On the other hand, anomaly detections in the images of the power transmission lines are also a difficult task for humans because they have to inspect all the captured images thoroughly. We propose a method of photographing corrosion products generated on the surface of ground wires by using photoluminescence. In addition, we propose an anomaly detection method using deep neural networks further to improve the accuracy and efficiency of the inspections. In this paper, we present the results of field tests conducted to verify the essential operation of the inspection robot and the anomaly detection methods.</p>
Journal
-
- IEEJ Transactions on Power and Energy
-
IEEJ Transactions on Power and Energy 144 (3), 244-249, 2024-03-01
The Institute of Electrical Engineers of Japan
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390580793844373504
-
- NII Book ID
- AN10136334
-
- ISSN
- 13488147
- 03854213
-
- NDL BIB ID
- 033376967
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- NDL
- Crossref
-
- Abstract License Flag
- Disallowed