@inproceedings{3da60c87e0b347e3be0b4e44893c8558,
title = "Belief reliability evaluation with uncertain information using max entropy principle",
abstract = "Belief reliability is a new reliability metric aiming to describe the reliability of components or systems affected by both aleatory uncertainty and epistemic uncertainty. A key point in belief reliability evaluation is to determine belief reliability distribution with uncertain information such as statistical characteristics of data. This paper will propose an optimal model to determine belief reliability distribution using the maximum entropy principle when k-th central moments of uncertain parameters can be obtained. An approximation algorithm based on the linear interpolation and the genetic algorithm is subsequently developed to solve the optimal model. The proposed approximation algorithm is validated by the existing max entropy theorem when only the first and second central moments are available. To further demonstrate the proposed model, it is used in the belief reliability evaluation of a suspension bridge under vessel-bridge collision when the central moments of its performance parameters are available.",
keywords = "Belief reliability distribution, Belief reliability evaluation, Genetic algorithm, Linear interpolation, Max entropy principle",
author = "Tianpei Zu and Meilin Wen and Rui Kang and Qingyuan Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 European Safety and Reliability Association. Published by Research Publishing, Singapore.; 29th European Safety and Reliability Conference, ESREL 2019 ; Conference date: 22-09-2019 Through 26-09-2019",
year = "2020",
doi = "10.3850/978-981-11-2724-3\_0134-cd",
language = "英语",
series = "Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019",
publisher = "Research Publishing Services",
pages = "2641--2647",
editor = "Michael Beer and Enrico Zio",
booktitle = "Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019",
}