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Reliability assessment of complex mechatronic systems using a modified nonparametric belief propagation algorithm

Author

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  • Zhong, X.
  • Ichchou, M.
  • Saidi, A.

Abstract

Various parametric skewed distributions are widely used to model the time-to-failure (TTF) in the reliability analysis of mechatronic systems, where many items are unobservable due to the high cost of testing. Estimating the parameters of those distributions becomes a challenge. Previous research has failed to consider this problem due to the difficulty of dependency modeling. Recently the methodology of Bayesian networks (BNs) has greatly contributed to the reliability analysis of complex systems. In this paper, the problem of system reliability assessment (SRA) is formulated as a BN considering the parameter uncertainty. As the quantitative specification of BN, a normal distribution representing the stochastic nature of TTF distribution is learned to capture the interactions between the basic items and their output items. The approximation inference of our continuous BN model is performed by a modified version of nonparametric belief propagation (NBP) which can avoid using a junction tree that is inefficient for the mechatronic case because of the large treewidth. After reasoning, we obtain the marginal posterior density of each TTF model parameter. Other information from diverse sources and expert priors can be easily incorporated in this SRA model to achieve more accurate results. Simulation in simple and complex cases of mechatronic systems demonstrates that the posterior of the parameter network fits the data well and the uncertainty passes effectively through our BN based SRA model by using the modified NBP.

Suggested Citation

  • Zhong, X. & Ichchou, M. & Saidi, A., 2010. "Reliability assessment of complex mechatronic systems using a modified nonparametric belief propagation algorithm," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1174-1185.
  • Handle: RePEc:eee:reensy:v:95:y:2010:i:11:p:1174-1185
    DOI: 10.1016/j.ress.2010.05.004
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    References listed on IDEAS

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    Cited by:

    1. Dongjin Lee & Rong Pan, 2017. "Predictive maintenance of complex system with multi-level reliability structure," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4785-4801, August.
    2. Chen, Bin & Xie, Lei & Li, Yongzhan & Gao, Baocheng, 2020. "Acoustical damage detection of wind turbine yaw system using Bayesian network," Renewable Energy, Elsevier, vol. 160(C), pages 1364-1372.
    3. G. S. Mahapatra & B. S. Mahapatra & P. K. Roy, 2016. "A new concept for fuzzy variable based non-linear programming problem with application on system reliability via genetic algorithm approach," Annals of Operations Research, Springer, vol. 247(2), pages 853-866, December.
    4. Peng, Weiwen & Huang, Hong-Zhong & Li, Yanfeng & Zuo, Ming J. & Xie, Min, 2013. "Life cycle reliability assessment of new products—A Bayesian model updating approach," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 109-119.
    5. Yontay, Petek & Pan, Rong, 2016. "A computational Bayesian approach to dependency assessment in system reliability," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 104-114.

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