Towards Uncertainty-aware Remaining Useful Life Prediction via Domain Adaptation

• Published in: 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) pp. 1625 - 1628
• Main Authors: Lee, Hyun Yong, Kim, Nac-Woo, Lee, Jun-Gi, Lee, Byung-Tak
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.10.2022
• Subjects: Adaptation models; Data models; domain adaptation; Maintenance engineering; Measurement; Monte Carlo methods; prediction; Predictive models; Remaining useful life; Uncertainty
• ISSN: 2162-1241
• DOI: 10.1109/ICTC55196.2022.9952565

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.