DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Woosung | ko |
dc.contributor.author | Huh, Hyunsuk | ko |
dc.contributor.author | Tama, Bayu Adhi | ko |
dc.contributor.author | Park, Gyusang | ko |
dc.contributor.author | Lee, Seungchul | ko |
dc.date.accessioned | 2023-09-13T03:01:40Z | - |
dc.date.available | 2023-09-13T03:01:40Z | - |
dc.date.created | 2023-09-13 | - |
dc.date.created | 2023-09-13 | - |
dc.date.created | 2023-09-13 | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE ACCESS, v.7, pp.92151 - 92160 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312557 | - |
dc.description.abstract | Detection and diagnosis of material degradation are of a complex and challenging task since it is presently hand-operated by a human. Therefore, it leads to misinterpretation and avoids correct classification and diagnosis. In this paper, we develop a computer-assisted detection method of material failure by utilizing a deep learning approach. A deep convolutional neural network (CNN) model, combined with an image processing technique, e.g., adaptive histogram equalization, is trained to classify a real-world turbine tube degradation image data set, which is retrieved from a power generation company. The experimental result demonstrates the effectiveness of the proposed approach with predictive classification accuracy is up to 99.99% in comparison with a shallow machine learning algorithm, e.g., linear SVM. Furthermore, performance evaluation of a deep CNN with and without an above-mentioned image processing technique is exhibited and benchmarked. We successfully demonstrate a novel application in constructing a deep-structure neural network model for material degradation diagnosis, which is not available in the current literature. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | A Neural Network Model for Material Degradation Detection and Diagnosis Using Microscopic Images | - |
dc.type | Article | - |
dc.identifier.wosid | 000477864400113 | - |
dc.identifier.scopusid | 2-s2.0-85071736033 | - |
dc.type.rims | ART | - |
dc.citation.volume | 7 | - |
dc.citation.beginningpage | 92151 | - |
dc.citation.endingpage | 92160 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2019.2927162 | - |
dc.contributor.localauthor | Lee, Seungchul | - |
dc.contributor.nonIdAuthor | Choi, Woosung | - |
dc.contributor.nonIdAuthor | Huh, Hyunsuk | - |
dc.contributor.nonIdAuthor | Tama, Bayu Adhi | - |
dc.contributor.nonIdAuthor | Park, Gyusang | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Material degradation | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | creep damage | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | histogram equalization | - |
dc.subject.keywordAuthor | boiler tube | - |
dc.subject.keywordAuthor | high temperature | - |
dc.subject.keywordPlus | ROTATING MACHINERY | - |
dc.subject.keywordPlus | FAULT | - |
dc.subject.keywordPlus | INTELLIGENCE | - |
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