A Novel Bayesian Update Method for Parameter Reconstruction of Remaining Useful Life Prognostics

Pengfei Wen, Shaowei Chen, Shuai Zhao, Yong Li, Yan Wang, Zhi Dou

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Abstract

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 decisionmaking. 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.
OriginalsprogEngelsk
Titel2019 IEEE International Conference on Prognostics and Health Management (ICPHM)
Antal sider8
ForlagIEEE
Publikationsdatojun. 2019
Artikelnummer8819377
ISBN (Trykt)978-1-5386-8358-3
DOI
StatusUdgivet - jun. 2019
Begivenhed2019 IEEE International Conference on Prognostics and Health Management (ICPHM) - San Francisco, CA, USA
Varighed: 17 jun. 201920 jun. 2019

Konference

Konference2019 IEEE International Conference on Prognostics and Health Management (ICPHM)
LokationSan Francisco, CA, USA
Periode17/06/201920/06/2019

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