A Deep Learning Approach to Prognostics of Rolling Element Bearings
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hur, Jang-Wook | - |
dc.contributor.author | Akpudo, Ugochukwu Ejike | - |
dc.date.available | 2021-04-29T08:41:31Z | - |
dc.date.created | 2020-06-16 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2229-838X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/19082 | - |
dc.description.abstract | The use of deep learning approaches for prognostics and remaining useful life predictions have become obviously prevalent. Artificial recurrent neural networks like the long short-term memory are popularly employed for forecasting, prognostics and health management practices, and in other fields of life. As an unsupervised learning approach, the efficiency of the long short-term memory for time-series predictive purposes is quite remarkable in contrast to standard feedforward neural networks. Virtually all mechanical systems consist mostly of rotating components which are by nature, prone to degradation/failure from known and uncertain causes. As a result, condition monitoring of these rolling element bearings is necessary in order to carry out prognostics and make necessary life predictions which guide safe and cost-effective decision making. Several studies have been conducted on effective approaches and methods for accurate prognostics of rolling element bearings; however, this paper presents a case study on rolling element bearing prognostics and degradation performance using an LSTM model. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | UNIV TUN HUSSEIN ONN MALAYSIA | - |
dc.title | A Deep Learning Approach to Prognostics of Rolling Element Bearings | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hur, Jang-Wook | - |
dc.contributor.affiliatedAuthor | Akpudo, Ugochukwu Ejike | - |
dc.identifier.doi | 10.30880/ijie.2020.12.03.021 | - |
dc.identifier.wosid | 000520040900021 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING, v.12, no.3, pp.178 - 186 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING | - |
dc.citation.title | INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING | - |
dc.citation.volume | 12 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 178 | - |
dc.citation.endPage | 186 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordAuthor | Bearing Degradation | - |
dc.subject.keywordAuthor | Long short-term memory | - |
dc.subject.keywordAuthor | Feature Extraction | - |
dc.subject.keywordAuthor | Prognostics | - |
dc.subject.keywordAuthor | Degradation assessment | - |
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