System Reliability Estimation of Divert Attitude Control System of a Launch Vehicle using Bayesian Networks
Divert attitude and control system (DACS) is a one-shot system and provides attitude correction and translation of the Launch vehicle. DACS consists of many flight critical sub systems which are arranged in a series configuration. The traditional Reliability block diagram and Fault tree diagram methods are unsuitable for reliability modelling, when considering uncertainty among the components and system. Bayesian network is the natural choice to model dependencies among the components and system. DACS being one shot system, it is very expensive and time consuming to test more number of systems during the design and development. Hence the data is drawn from component level, subsystem level and expert opinion is used for reliability estimation. In this paper, Bayesian network modelling of DAC system was carried out for estimating the reliability using multi-level data. An algorithm is developed for computation of Conditional probabilities in Bayesian network. Posterior probability distribution of components is calculated using Markov Chain Monte Carlo (MCMC) simulations and results are compared with Junction tree based exact inference algorithm. MATLAB code is developed to estimate the reliability of DAC system.
A.G.Wilson, T.L.Graves, M.S.Hamada and C.S.Reese, "Advances in data combination, analysis and collection for system reliability assessment,” Statistical science,21(4),514-531,2006.
Alyson G.Wilson, Laura A.McNamara,GD.Wilson,"Information integration for complex systems" Reliability Engineering and System Safety 92(2007) 121-130.
A.G. Wilson, A.V. Huzurbazar. A Bayesian networks for multilevel system reliability. Reliability Engineering and System Safety 92 (2007) 1413–1420.
A.G. Wilson,& Kassandra M.Fronczyk(2017) "Bayesian Reliability: combining information", Quality Engineering ,29:1119-129,DOI:10.1080/08982112.201.1211889
Hamada M, Martz H, Reese CS, Graves T, Johnson V, Wilson A, “A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation”, Reliability Engineering and System Safety 2004; 86:297–305.
Jiqiang Guo, Alyson G. Wilson, “Bayesian Methods for Estimating the Reliability of Complex Systems Using Heterogeneous Multilevel Information”, Dept. of Statistics Iowa State University Ames, Iowa, 50011 (2013).
Roy Billinton, R N Allan, Reliability Evaluation of Engineering systems, Indian reprint edition B.S Publications (2007).
Sheng Zhai, Shuzhong Lin, “Bayesian Networks Application in Reliability analysis of multi-state system”, School of Mechanical Engineering Tianjin Polytechnic University Tianjin, P.R. China (2013).
Yu Liu, Peng Lin, Yan-Feng Li, and Hong-Zhong Huang, “Bayesian Reliability and Performance Assessment for Multi-State Systems”, University of Electronic Science and Technology of China, (2015).
Duan Zhou, Rong Pan, Chair Daniel McCarville Muhong Zhang, “The Application of Bayesian Networks in System Reliability”, Arizona state university (2014).
Petek Yontay, Rong Pan, “A Computational Bayesian Approach to Dependency Assessment in System Reliability”, Arizona State University School of Computing, Informatics, and Decision Systems Engineering, 699 S. Mill Avenue, Tempe, AZ 85281, USA, (2016).
V.Sankar, System Reliability Concepts, Himalaya Publications House, (2015).
Balaram Das, “Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem”, Command and Control Division, DSTO, Edinburgh, SA 5111, Australia.
A.G. Wilson, A.V. Huzurbazar, “A Bayesian networks for multi-level system reliability”, Reliability Engineering and System Safety 92 (2007) 1413–1420.
H Guo,T Jin ,A Mettas" Deigning reliability demonstration tests for one shot systems under zero component failures" IEEE Transactions on reliability,Vol .60,No.1,286-294March 2011.
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