Time-variant reliability analysis based on high dimensional model representation

Kai Cheng and Zhenzhou Lu

Reliability Engineering and System Safety, 2019, vol. 188, issue C, 310-319

Abstract: Time-variant reliability analysis aims at estimating the probability that an engineering system successfully performs intended missions over a certain period of time under various sources of uncertainty. In order to perform the time-variant reliability analysis efficiently, this paper presents a high dimensional model representation (HDMR) model combined with an active learning strategy to estimate the failure probability of dynamic problem. Firstly, the HDMR meta-model is established in the augmented input space (random variables and time) based on Gaussian process regression technique. Then, a reliability analysis approach incorporating epistemic uncertainty is proposed, and a learning function is introduced to update the experimental design sequentially. Finally, the Monte Carlo simulation method is applied for time-variant failure probability assessment based on the well-developed HDMR meta-model. Two engineering applications are used to demonstrate the effectiveness of the proposed method for time-variant reliability analysis.

Keywords: Time-variant reliability; Gaussian process; Active learning; High dimensional model representation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:188:y:2019:i:c:p:310-319

DOI: 10.1016/j.ress.2019.03.041

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