TY - GEN
T1 - A novel Bayesian update method for parameter-reconstruction of remaining useful life prognostics
AU - Wen, Pengfei
AU - Chen, Shaowei
AU - Zhao, Shuai
AU - Li, Yong
AU - Wang, Yan
AU - Dou, Zhi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - —Remaining useful life (RUL) prediction is a core component for reliability research and condition-based maintenance (CBM). In the existing parameter-reconstruction method, the degradation trajectory of an in-situ unit is reconstructed by the weighted sum of that of historical units. However, this method requires an optimization problem to be solved for each new measurement, which leads to an excessively consumed time and does not satisfy the requirements of online prognostics and decision-making. In this paper, these weights are assumed as a set of probabilities, based on which they can be updated via Bayesian estimation, instead of solving the optimization problem at each observation epoch. To verify the proposed approach, a data set developed by a commercial simulation tool for aircraft turbofan engines is involved. In light of the implement situation of the proposed approach on this data set, the absolute error of the prognostics result and the consumed time for computation are significantly reduced compared with the existing approach.
AB - —Remaining useful life (RUL) prediction is a core component for reliability research and condition-based maintenance (CBM). In the existing parameter-reconstruction method, the degradation trajectory of an in-situ unit is reconstructed by the weighted sum of that of historical units. However, this method requires an optimization problem to be solved for each new measurement, which leads to an excessively consumed time and does not satisfy the requirements of online prognostics and decision-making. In this paper, these weights are assumed as a set of probabilities, based on which they can be updated via Bayesian estimation, instead of solving the optimization problem at each observation epoch. To verify the proposed approach, a data set developed by a commercial simulation tool for aircraft turbofan engines is involved. In light of the implement situation of the proposed approach on this data set, the absolute error of the prognostics result and the consumed time for computation are significantly reduced compared with the existing approach.
KW - -online prognostics
KW - Bayesian update
KW - Reconstruction
KW - RUL estimation
UR - http://www.scopus.com/inward/record.url?scp=85072782530&partnerID=8YFLogxK
U2 - 10.1109/ICPHM.2019.8819377
DO - 10.1109/ICPHM.2019.8819377
M3 - 会议稿件
AN - SCOPUS:85072782530
T3 - 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
BT - 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
Y2 - 17 June 2019 through 20 June 2019
ER -