Damage Detection With an Ultrasound Array and Deep Convolutional Neural Network Fusion
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Donggeun | - |
dc.contributor.author | Kim, San | - |
dc.contributor.author | Jeong, Siheon | - |
dc.contributor.author | Ham, Ji-Wan | - |
dc.contributor.author | Son, Seho | - |
dc.contributor.author | Oh, Ki-Yong | - |
dc.date.accessioned | 2022-01-19T01:43:09Z | - |
dc.date.available | 2022-01-19T01:43:09Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53801 | - |
dc.description.abstract | Diagnostic methods for power transmission facilities are important for energy security because the growth of defects in power facilities increases the risk of blackouts in an entire power grid. However, damage in power transmission facilities is difficult to detect because cracks or defects are minuscule and are challenging to determine. One interesting phenomenon caused by damage in power transmission facilities is ultrasound emissions on a damaged surface. However, measuring ultrasound emissions to detect defects is limited by the severity of the surrounding noise. To overcome this limitation, this study proposes a new method for damage detection by fusing ultrasound measurements with recorded optical images. The proposed method consists of two phases. The first phase preprocesses ultrasound measurements for ultrasound feature extraction. This phase aims to detect the location of ultrasound emissions by analyzing ultrasound characteristics including the intensity and density. The second phase detects and classifies a damaged object with optical images recorded using a deep convolutional neural network. This phase not only discards the noise from the ultrasound measurements but also classifies a damaged system among many components in power transmission facilities. The experiments validate the effectiveness of the proposed method using ultrasound measurements and recorded images and finally suggest scenarios for potential applications. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Damage Detection With an Ultrasound Array and Deep Convolutional Neural Network Fusion | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3032030 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp 189423 - 189435 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000584821700001 | - |
dc.identifier.scopusid | 2-s2.0-85102735809 | - |
dc.citation.endPage | 189435 | - |
dc.citation.startPage | 189423 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Health management | - |
dc.subject.keywordAuthor | diagnostics | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | damage detection | - |
dc.subject.keywordAuthor | feature extraction | - |
dc.subject.keywordAuthor | ultrasound camera | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.