| Download | - View accepted manuscript: Application of deep-learning via transfer learning to evaluate silicone rubber material surface erosion (PDF, 617 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1109/TDEI.2021.009617 |
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| Author | Search for: El Haj, Youssef; Search for: El-Hag, Ayman H.; Search for: Ghunem, Refat A.1 |
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| Affiliation | - National Research Council of Canada. Metrology Research Centre
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| Format | Text, Article |
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| Subject | silicone rubber; outdoor insulators; transmission and distribution; condition monitoring; deep learning |
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| Abstract | In this letter a deep learning-based model is proposed for online inspection of silicone rubber outdoor insulators. The inclined plane tracking and erosion test is used as per ASTM D2303 in order to simulate standard erosion on silicone rubber insulation composites. Photos taken for the tested composites are used as training and testing inputs for a convolutional neural network topology in the proposed deep learning model, thereby classifying the degree of erosion damage into light, moderate and severe. The remarkable classification accuracy obtained shows the potential of utilizing the proposed framework for online monitoring of outdoor silicone rubber insulators in the transmission and distribution grid. |
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| Publication date | 2021-08-17 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 8d69ff91-ff1a-4214-87cc-0d84ea990c2b |
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| Record created | 2021-10-07 |
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| Record modified | 2021-10-08 |
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