DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Haedong | - |
dc.contributor.author | Park, Seungtae | - |
dc.contributor.author | Woo, Sunhee | - |
dc.contributor.author | Lee, Seungchul | - |
dc.date.accessioned | 2019-03-07T01:20:50Z | - |
dc.date.available | 2019-03-07T01:20:50Z | - |
dc.date.created | 2019-01-21 | - |
dc.date.issued | 2016-08 | - |
dc.identifier.issn | 2351-9789 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/95018 | - |
dc.description.abstract | Although the orbit analysis (orbit shape and size) is commonly used to diagnose rotating machinery, the diagnosis heavily depends on the expert knowledge or experience due to the difficulties of extracting mathematical features for data-driven approaches. Therefore, in this paper, we propose an autonomous orbit pattern recognition algorithm using the deep learning method on shaft orbit shape images. In details, the convolutional neural network is implemented to construct weights between neurons and to generate the entire structure of the neural network. Then, the created network enables us to classify fault modes of rotating machinery via orbit images. Furthermore, we demonstrate the proposed framework through a rotating testbed. | - |
dc.language | English | - |
dc.publisher | ELSEVIER | - |
dc.relation.isPartOf | Procedia Manufacturing | - |
dc.title | Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.promfg.2016.08.083 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Procedia Manufacturing, v.5, pp.1107 - 1118 | - |
dc.identifier.wosid | 000387592400084 | - |
dc.citation.endPage | 1118 | - |
dc.citation.startPage | 1107 | - |
dc.citation.title | Procedia Manufacturing | - |
dc.citation.volume | 5 | - |
dc.contributor.affiliatedAuthor | Lee, Seungchul | - |
dc.identifier.scopusid | 2-s2.0-85014482598 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Convolutional Neural Networks | - |
dc.subject.keywordAuthor | Rotating Machinery | - |
dc.subject.keywordAuthor | Orbit Analysis | - |
dc.subject.keywordAuthor | Image Pattern Recognition | - |
dc.subject.keywordAuthor | Machine Learning | - |
dc.relation.journalWebOfScienceCategory | Engineering, Manufacturing | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
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