Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network
The fourth industrial revolution - Industry 4.0 - puts emphasis on the application of intelligent technologies in the area of monitoring and identification of electrical equipment. High precision and non-contact qualities make the infrared thermography one of the most suitable technologies for intel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9146829/ |
| _version_ | 1818618612126056448 |
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| author | Zhewen Niu Marek Z. Reformat Wenhu Tang Baining Zhao |
| author_facet | Zhewen Niu Marek Z. Reformat Wenhu Tang Baining Zhao |
| author_sort | Zhewen Niu |
| collection | DOAJ |
| description | The fourth industrial revolution - Industry 4.0 - puts emphasis on the application of intelligent technologies in the area of monitoring and identification of electrical equipment. High precision and non-contact qualities make the infrared thermography one of the most suitable technologies for intelligent inspection of high-voltage apparatus. Yet, due to imperfect data acquisition methods and difficulties in collecting data, electrical equipment images are limited in quantities and imbalanced in representing different types of devices. Additionally, it is not easy to extract representative features of infrared images due to their low-intensity contrast and uneven distribution. In this paper, a data-driven framework is proposed for the identification of electrical equipment based on infrared images. The main technique of this proposed system is a novel process of generating synthetic infrared images. For this purpose, an Edge-Oriented Generative Adversarial Network (EOGAN) is developed. The EOGAN is designed to create realistic infrared images that can be used as augmented data for developing data-driven identification methods. Extracted edge features of electrical equipment are utilized as prior information to guide the process of generating realistic infrared images. Finally, comparative experiments are carried out to show the effectiveness of the proposed EOGAN-based framework for equipment identification in the presence of limited and imbalanced image datasets. |
| first_indexed | 2024-12-16T17:24:21Z |
| format | Article |
| id | doaj.art-288dd169aaa745328950de456f90512c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:24:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-288dd169aaa745328950de456f90512c2022-12-21T22:23:06ZengIEEEIEEE Access2169-35362020-01-01813648713649710.1109/ACCESS.2020.30116899146829Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial NetworkZhewen Niu0https://orcid.org/0000-0002-4859-1511Marek Z. Reformat1https://orcid.org/0000-0003-4783-0717Wenhu Tang2https://orcid.org/0000-0003-1823-2355Baining Zhao3https://orcid.org/0000-0002-9653-3316School of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, CanadaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou, ChinaThe fourth industrial revolution - Industry 4.0 - puts emphasis on the application of intelligent technologies in the area of monitoring and identification of electrical equipment. High precision and non-contact qualities make the infrared thermography one of the most suitable technologies for intelligent inspection of high-voltage apparatus. Yet, due to imperfect data acquisition methods and difficulties in collecting data, electrical equipment images are limited in quantities and imbalanced in representing different types of devices. Additionally, it is not easy to extract representative features of infrared images due to their low-intensity contrast and uneven distribution. In this paper, a data-driven framework is proposed for the identification of electrical equipment based on infrared images. The main technique of this proposed system is a novel process of generating synthetic infrared images. For this purpose, an Edge-Oriented Generative Adversarial Network (EOGAN) is developed. The EOGAN is designed to create realistic infrared images that can be used as augmented data for developing data-driven identification methods. Extracted edge features of electrical equipment are utilized as prior information to guide the process of generating realistic infrared images. Finally, comparative experiments are carried out to show the effectiveness of the proposed EOGAN-based framework for equipment identification in the presence of limited and imbalanced image datasets.https://ieeexplore.ieee.org/document/9146829/Edge prior knowledgeelectrical equipment identificationgenerative adversarial networkinfrared image |
| spellingShingle | Zhewen Niu Marek Z. Reformat Wenhu Tang Baining Zhao Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network IEEE Access Edge prior knowledge electrical equipment identification generative adversarial network infrared image |
| title | Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network |
| title_full | Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network |
| title_fullStr | Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network |
| title_full_unstemmed | Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network |
| title_short | Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network |
| title_sort | electrical equipment identification method with synthetic data using edge oriented generative adversarial network |
| topic | Edge prior knowledge electrical equipment identification generative adversarial network infrared image |
| url | https://ieeexplore.ieee.org/document/9146829/ |
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