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A Time-series Data Generation Method to Predict Remaining Useful Life

Title
A Time-series Data Generation Method to Predict Remaining Useful Life
Author
허선
Keywords
remaining useful life prediction; data generation; symbolic aggregate approximation; run-to-failure
Issue Date
2021-06
Publisher
MDPI
Citation
PROCESSES, v. 9, no. 7, Article no. 1115, 19pp
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. View Full-Text
URI
https://www.mdpi.com/2227-9717/9/7/1115https://repository.hanyang.ac.kr/handle/20.500.11754/166571
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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