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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
