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학술대회 Towards Uncertainty-aware Remaining Useful Life Prediction via Domain Adaptation
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저자
이현용, 김낙우, 이준기, 이병탁
발행일
202210
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1625-1628
DOI
https://dx.doi.org/10.1109/ICTC55196.2022.9952565
협약과제
22ZK1100, 호남권 지역산업 기반 ICT 융합기술 고도화 지원사업, 강현서
초록
Predicting accurate remaining useful life (RUL) is quite necessary in many industrial cases. Recently, data-driven method has been widely used for RUL prediction because of its promising performance and ease of use. However, one practical challenging issue of the data-driven method is lack of labeled data. One way for dealing with this issue is domain adaptation. With domain adaptation technique, a RUL prediction model is built using labeled data of source domain and then the trained model is used to predict RUL of target domain where labeled data is unavailable. In this paper, we propose our method for RUL prediction through domain adaptation. Another practical issue of RUL prediction is that a single RUL prediction value may not be enough for successful maintenance of target systems. In other words, we need to know how uncertain RUL prediction is. In this paper, we study Monte Carlo (MC) dropout-based uncertainty metric for realizing better RUL prediction result. Through experiment, we first show that our domain adaptation technique shows reasonable performance. We also show that MC dropout-based approach generates the uncertainty metric only meaningful for source domain.
KSP 제안 키워드
Based Approach, Ease of use, Labeled data, Monte carlo, RUL prediction model, Remaining useful life prediction, Source Domain, Target domain, data-driven method, domain adaptation, uncertainty metric