Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 허선 | - |
dc.date.accessioned | 2022-05-03T23:58:55Z | - |
dc.date.available | 2022-05-03T23:58:55Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.citation | Processes, Vol 9, Iss 1115, p 1115 (2021) | en_US |
dc.identifier.issn | 2227-9717 | - |
dc.identifier.uri | https://www.proquest.com/docview/2579126866/fulltextPDF/DC10885B4CD1477EPQ/1?accountid=11283 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/170539 | - |
dc.description.abstract | Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government ((MSIT)2019R1A2C1088255). | en_US |
dc.language.iso | en | en_US |
dc.publisher | PHM 학회 | en_US |
dc.subject | remaining useful life prediction | en_US |
dc.subject | data generation | en_US |
dc.subject | symbolic aggregate approximation | en_US |
dc.subject | run-to-failure | en_US |
dc.subject | Chemical technology | en_US |
dc.subject | TP1-1185 | en_US |
dc.subject | Chemistry | en_US |
dc.subject | QD1-999 | en_US |
dc.title | A Time Series Data Generation Method for Remaining Useful Life | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/pr9071115 | - |
dc.relation.page | 1-4 | - |
dc.contributor.googleauthor | Ahn, Gilseung | - |
dc.contributor.googleauthor | Yun, Hyungseok | - |
dc.contributor.googleauthor | Hur, Sun | - |
dc.contributor.googleauthor | Lim, Siyeong | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING | - |
dc.identifier.pid | hursun | - |
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