Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 156, Issue -, Pages 51-58Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2016.07.021
Keywords
Uncertain inverse problem; Point estimation; Maximum entropy; Uncertainty propagation; Stochastic structure
Funding
- National Natural Science Foundation of China [11572115, 11232004]
- independent research project of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University [51475003]
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In this paper, a kind of inverse problem for assessing the probabilities of identified parameters with uncertainties in structural parameters and limited experimental results is investigated. The point estimation method and maximum entropy principle are adopted to efficiently evaluate the effect of uncertain parameters on the identified parameters. First, the probability distribution function of each uncertain parameter can be approximately represented by several nodes. Thus, the uncertain inverse problem can be transformed into several deterministic inverse problems through multivariate Taylor expansion and point estimation method. Then, to obtain the moments of identified parameters, the deterministic inverse process for each selected node with concentrated probability are conducted by the genetic algorithm. Finally, the probability distribution functions of the identified parameters can be assessed by the obtained moments based on the maximum entropy principle. The proposed method effectively avoids the low efficiency of uncertain inverse problem, which commonly involves a double loop procedure with uncertainty propagation and inverse calculation. Numerical examples and the engineering application demonstrate the feasibility and effectiveness of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.
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