Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions
Overhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/22/5595 |
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| author | Arailym Serikbay Mehdi Bagheri Amin Zollanvari B. T. Phung |
| author_facet | Arailym Serikbay Mehdi Bagheri Amin Zollanvari B. T. Phung |
| author_sort | Arailym Serikbay |
| collection | DOAJ |
| description | Overhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failure, regular inspections are essential to prevent power outages. To this end, this paper proposes a novel technique based on deep convolutional neural networks (CNNs) to classify high-voltage insulator surface conditions based on their image. Successful applications of CNNs in computer vision have led to several pretrained architectures for image classification. To use these pretrained models, a practitioner typically fine-tunes and selects one final model via a model selection stage and discards all other models. In contrast with many existing studies that use such a “winner-takes-all” approach, here, we identify the best subset of seven popular pretrained CNN architectures that are combined by soft voting to form an ensemble classifier. From a machine learning (ML) perspective, this focus is warranted because the convolutional base of each pretrained architecture operates as a feature extractor and an ensemble of them works as a combination of various feature extraction rules. Our numerical experiments demonstrate the advantage of the identified ensemble model to individual pretrained architectures. |
| format | Article |
| id | doaj-art-a06ddfa5f24847509611125a258200b3 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-a06ddfa5f24847509611125a258200b32025-08-20T02:08:15ZengMDPI AGEnergies1996-10732024-11-011722559510.3390/en17225595Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface ConditionsArailym Serikbay0Mehdi Bagheri1Amin Zollanvari2B. T. Phung3Department of Electrical and Computer Engineering, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, KazakhstanDepartment of Electrical and Computer Engineering, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, KazakhstanDepartment of Electrical and Computer Engineering, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, KazakhstanSchool of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, AustraliaOverhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failure, regular inspections are essential to prevent power outages. To this end, this paper proposes a novel technique based on deep convolutional neural networks (CNNs) to classify high-voltage insulator surface conditions based on their image. Successful applications of CNNs in computer vision have led to several pretrained architectures for image classification. To use these pretrained models, a practitioner typically fine-tunes and selects one final model via a model selection stage and discards all other models. In contrast with many existing studies that use such a “winner-takes-all” approach, here, we identify the best subset of seven popular pretrained CNN architectures that are combined by soft voting to form an ensemble classifier. From a machine learning (ML) perspective, this focus is warranted because the convolutional base of each pretrained architecture operates as a feature extractor and an ensemble of them works as a combination of various feature extraction rules. Our numerical experiments demonstrate the advantage of the identified ensemble model to individual pretrained architectures.https://www.mdpi.com/1996-1073/17/22/5595deep learningensemble learningcondition assessmenthigh-voltage insulatorscontamination classification |
| spellingShingle | Arailym Serikbay Mehdi Bagheri Amin Zollanvari B. T. Phung Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions Energies deep learning ensemble learning condition assessment high-voltage insulators contamination classification |
| title | Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions |
| title_full | Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions |
| title_fullStr | Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions |
| title_full_unstemmed | Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions |
| title_short | Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions |
| title_sort | ensemble pretrained convolutional neural networks for the classification of insulator surface conditions |
| topic | deep learning ensemble learning condition assessment high-voltage insulators contamination classification |
| url | https://www.mdpi.com/1996-1073/17/22/5595 |
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