A wavelet packet spectral subtraction and convolutional neural network based method for diagnosis of system health
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Van Huan Pham | - |
dc.contributor.author | Han, Soonyoung | - |
dc.contributor.author | Minh Duc Do | - |
dc.contributor.author | Choi, Hae-Jin | - |
dc.date.available | 2020-04-17T04:21:07Z | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 1738-494X | - |
dc.identifier.issn | 1976-3824 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38612 | - |
dc.description.abstract | Health monitoring systems play a key role inside smart factories. To enhance the real-time capability and reliability of health monitoring systems, we propose a fully automatic method for machine diagnosis. Firstly, acquired vibration signals are converted into high-resolution images by wavelet packet spectral subtraction. Next, a trained convolutional neural network (CNN) automatically extracts important features and determines the current health of the machine. The performance of the proposed method is demonstrated by employing a diagnosis problem of a bearing system. The result shows an outstanding classification accuracy of 99.64 % even with a small amount of training data (5 % of the data). | - |
dc.format.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | KOREAN SOC MECHANICAL ENGINEERS | - |
dc.title | A wavelet packet spectral subtraction and convolutional neural network based method for diagnosis of system health | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s12206-019-1111-6 | - |
dc.identifier.bibliographicCitation | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.33, no.12, pp 5683 - 5687 | - |
dc.identifier.kciid | ART002529392 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000504965100011 | - |
dc.identifier.scopusid | 2-s2.0-85077157269 | - |
dc.citation.endPage | 5687 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 5683 | - |
dc.citation.title | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | - |
dc.citation.volume | 33 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Diagnosis | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Wavelet packet decomposition | - |
dc.subject.keywordAuthor | Vibration signal | - |
dc.subject.keywordAuthor | Spectral subtraction | - |
dc.subject.keywordAuthor | Prognosis health management | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Mechanical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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