Abstract
In this paper, for the problem of how to improve the accuracy of Bayesian small sample reliability evaluation, we improve the pre-test information preprocessing and Bayesian reliability evaluation. In the preprocessing stage of pre-test information, the conversion of pre-test information is mainly studied. Aiming at the problem of information conversion in similar systems, this paper presents a novel method, which based on the correlation coefficient to determine the relationship between similar systems, and the use of D-S evidence theory to integrate the conversion method; At the same time, In this paper, an improved method based on improved HS algorithm is proposed to improve the accuracy of information partitioning and matching, and then improve the conversion efficiency. In the Bayesian reliability evaluation stage, the distribution of pre-test information is determined by using the method of conjugate pre-distribution distribution, and a mixed pre-test model is proposed to solve the problem of pre-test information “submerged” small sample field test information and the problem of multi-source pre-test distribution of the weight; and the evaluation results of the reliability parameters are obtained effectively.
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Acknowledgments
This work was funded by the National Natural Science Foundation of China under Grant (No. 61772152 and No. 61502037), the Basic Research Project (No. JCKY2016206B001, JCKY2014206C002 and JCKY2017604C010), and the Technical Foundation Project (No. JSQB2017206C002).
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Wang, H., Wang, N., Zhou, L., Gu, Z., Dang, R. (2018). A Method of Small Sample Reliability Assessment Based on Bayesian Theory. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_11
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DOI: https://doi.org/10.1007/978-3-030-00006-6_11
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