Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique
This paper presents a novel artificial neural network (ANN)-based near-field microwave nondestructive testing technique for defect detection and classification in nonceramic insulators (NCI). In this paper, distribution class 33-kV NCI samples with no defects, air voids in silicone rubber and fiber...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
2014
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/8552 |
| الوسوم: |
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| _version_ | 1864513444525375488 |
|---|---|
| author | Qaddoumi, Nasser |
| author2 | El-Hag, Ayman Saker, Yasser |
| author2_role | author author |
| author_facet | Qaddoumi, Nasser El-Hag, Ayman Saker, Yasser |
| author_role | author |
| dc.creator.none.fl_str_mv | Qaddoumi, Nasser El-Hag, Ayman Saker, Yasser |
| dc.date.none.fl_str_mv | 2014-02 2016-10-19T10:31:21Z 2016-10-19T10:31:21Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Qaddoumi, Naser, Ayman El-Hag, and Y. Saker. "Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique." IEEE Transactions on Instrumentation and Measurement 63, no. 2 (2014): 260 - 266 http://hdl.handle.net/11073/8552 10.1109/TIM.2013.2280486 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | IEEE Transactions on Instrumentation and Measurement https://dx.doi.org/10.1109/TIM.2013.2280486 |
| dc.subject.none.fl_str_mv | Microwave theory and techniques Microwave imaging Feature extraction Microwave measurement Artificial neural networks Insulators Materials |
| dc.title.none.fl_str_mv | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper presents a novel artificial neural network (ANN)-based near-field microwave nondestructive testing technique for defect detection and classification in nonceramic insulators (NCI). In this paper, distribution class 33-kV NCI samples with no defects, air voids in silicone rubber and fiber glass core, cracks in the fiberglass core, and small metallic inclusion between the fiber core and shank were inspected. The microwave inspection system uses an open-ended rectangular waveguide sensor operating in the near-field at a frequency of 24 GHz. A data acquisition system was used to record the measured data. ANN was trained to classify the different types of defects. The results showed that all defects were detected and classified correctly with high recognition rates. |
| format | article |
| id | aus_5818629bfaca8b5effa763119ad9a12c |
| identifier_str_mv | Qaddoumi, Naser, Ayman El-Hag, and Y. Saker. "Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique." IEEE Transactions on Instrumentation and Measurement 63, no. 2 (2014): 260 - 266 10.1109/TIM.2013.2280486 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8552 |
| publishDate | 2014 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave TechniqueQaddoumi, NasserEl-Hag, AymanSaker, YasserMicrowave theory and techniquesMicrowave imagingFeature extractionMicrowave measurementArtificial neural networksInsulatorsMaterialsThis paper presents a novel artificial neural network (ANN)-based near-field microwave nondestructive testing technique for defect detection and classification in nonceramic insulators (NCI). In this paper, distribution class 33-kV NCI samples with no defects, air voids in silicone rubber and fiber glass core, cracks in the fiberglass core, and small metallic inclusion between the fiber core and shank were inspected. The microwave inspection system uses an open-ended rectangular waveguide sensor operating in the near-field at a frequency of 24 GHz. A data acquisition system was used to record the measured data. ANN was trained to classify the different types of defects. The results showed that all defects were detected and classified correctly with high recognition rates.2016-10-19T10:31:21Z2016-10-19T10:31:21Z2014-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfQaddoumi, Naser, Ayman El-Hag, and Y. Saker. "Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique." IEEE Transactions on Instrumentation and Measurement 63, no. 2 (2014): 260 - 266http://hdl.handle.net/11073/855210.1109/TIM.2013.2280486en_USIEEE Transactions on Instrumentation and Measurementhttps://dx.doi.org/10.1109/TIM.2013.2280486oai:repository.aus.edu:11073/85522024-08-22T12:18:50Z |
| spellingShingle | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique Qaddoumi, Nasser Microwave theory and techniques Microwave imaging Feature extraction Microwave measurement Artificial neural networks Insulators Materials |
| status_str | publishedVersion |
| title | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique |
| title_full | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique |
| title_fullStr | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique |
| title_full_unstemmed | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique |
| title_short | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique |
| title_sort | Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique |
| topic | Microwave theory and techniques Microwave imaging Feature extraction Microwave measurement Artificial neural networks Insulators Materials |
| url | http://hdl.handle.net/11073/8552 |