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Volume 42 Issue 1
Jan.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(1): 101-111.
HAO Zhipeng, ZENG Shengkui, GUO Jianbin, et al. Integrated method of knowledge and data for quantitative reliability modeling[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(1): 101-111. doi: 10.13700/j.bh.1001-5965.2015.0029(in Chinese)
Citation: HAO Zhipeng, ZENG Shengkui, GUO Jianbin, et al. Integrated method of knowledge and data for quantitative reliability modeling[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(1): 101-111. doi: 10.13700/j.bh.1001-5965.2015.0029(in Chinese)
shu

Integrated method of knowledge and data for quantitative reliability modeling

doi: 10.13700/j.bh.1001-5965.2015.0029
  • 1.

    School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 2. Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 3. Sino-French Engineer School, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 4. Jincheng Nanjing Electrical and Hydraulic Engineering Research Center, AVIC, Nanjing 211140, China

Funds:  National Natural Science Foundation of China(61304218)
  • Received Date: 15 Jan 2015
  • Publish Date: 20 Jan 2016
    Abstract
  • Keys of quantitative reliability design lie in the establishment of the quantitative reliability model. Current modeling methods mainly rely on design staff's knowledge on product failure rules, including failure modes, environmental disturbances, failure mechanisms, etc. However, the inherent finiteness and imperfection of knowledge are bound to bring both model error and input uncertainties to the quantitative reliability model. To address this problem, we proposed a knowledge-and-data integrated Bayesian modeling method to develop the quantitative reliability model, quantifying the model error and input uncertainties. First of all, the tasks of the knowledge-and-data integrated modeling of the quantitative reliability model were explained, and the corresponding framework was established. Then the principle of Bayesian integration of knowledge and data was clarified. After that, the general method of Bayesian integration of knowledge and data was proposed, and two specific Bayesian integration methods for both performance fluctuation and degradation data were addressed respectively. Finally, the effectiveness and feasibility of the proposed method were illustrated by a case of an airborne axial piston pump.

     

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