A Time Series Data Generation Method for Remaining Useful Life
- Title
- A Time Series Data Generation Method for Remaining Useful Life
- Author
- 허선
- Keywords
- remaining useful life prediction; data generation; symbolic aggregate approximation; run-to-failure; Chemical technology; TP1-1185; Chemistry; QD1-999
- Issue Date
- 2021-09
- Publisher
- PHM 학회
- Citation
- Processes, Vol 9, Iss 1115, p 1115 (2021)
- 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.
- URI
- https://www.proquest.com/docview/2579126866/fulltextPDF/DC10885B4CD1477EPQ/1?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/170539
- ISSN
- 2227-9717
- DOI
- 10.3390/pr9071115
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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