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Probabilistic information fusion with point, moment and interval data in reliability assessment

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  • Zhou, Daoqing
  • He, Jingjing
  • Du, Yi-Mu
  • Sun, C.P.
  • Guan, Xuefei

Abstract

This study presents a general framework for probabilistic information fusion with point, moment, and interval data based on the principle of maximum relative entropy. Two types of interval information, namely, the independent interval and the correlated interval, are naturally incorporated in this framework for probability inference. The relative entropy is alternatively expressed using the hazard rate functions associated with the distributions. The probabilistic information fusion problem is recast into a hazard rate dynamics problem, which is solved using Euler-Lagrange method with point, moment, and interval data as boundary conditions. It provides a novel perspective on probabilistic information fusion such that the fusion mechanism is to seek an optimal hazard rate function in the functional space achieving the least action which is expressed as the relative information entropy. The geometry interpretation of the information fusion with point, moment, and interval data, and the effect of processing sequence are signified. An electronic component reliability problem is used to illustrate the basic idea of the method, followed by a fatigue reliability assessment problem demonstrating the overall method. The effectiveness of the method using limited samples and implicit interval information is emphasized using an aeroengine disk lifing application with a risk requirement in airworthiness.

Suggested Citation

  • Zhou, Daoqing & He, Jingjing & Du, Yi-Mu & Sun, C.P. & Guan, Xuefei, 2021. "Probabilistic information fusion with point, moment and interval data in reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021003148
    DOI: 10.1016/j.ress.2021.107790
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    1. Choi, Woosung & Youn, Byeng D. & Oh, Hyunseok & Kim, Nam H., 2019. "A Bayesian approach for a damage growth model using sporadically measured and heterogeneous on-site data from a steam turbine," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 137-150.
    2. Giffin, Adom & Cafaro, Carlo & Ali, Sean Alan, 2016. "Application of the maximum relative entropy method to the physics of ferromagnetic materials," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 455(C), pages 11-26.
    3. Xu, Hao & Gardoni, Paolo, 2020. "Conditional formulation for the calibration of multi-level random fields with incomplete data," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Zhou, Ying & Li, Chenshuang & Zhou, Cheng & Luo, Hanbin, 2018. "Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 152-167.
    5. Giffin, Adom, 2009. "From physics to economics: An econometric example using maximum relative entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1610-1620.
    6. Adom Giffin, 2009. "From Physics to Economics: An Econometric Example Using Maximum Relative Entropy," Papers 0901.0401, arXiv.org.
    7. VanDerHorn, Eric & Mahadevan, Sankaran, 2018. "Bayesian model updating with summarized statistical and reliability data," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 12-24.
    8. Sýkora, Miroslav & Marková, Jana & Diamantidis, Dimitris, 2018. "Bayesian network application for the risk assessment of existing energy production units," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 312-320.
    9. Li, Chenzhao & Mahadevan, Sankaran, 2018. "Efficient approximate inference in Bayesian networks with continuous variables," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 269-280.
    10. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    11. Guan, Xuefei & He, Jingjing & Jha, Ratneshwar & Liu, Yongming, 2012. "An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 1-13.
    12. Hossain, Niamat Ullah Ibne & Nur, Farjana & Hosseini, Seyedmohsen & Jaradat, Raed & Marufuzzaman, Mohammad & Puryear, Stephen M., 2019. "A Bayesian network based approach for modeling and assessing resilience: A case study of a full service deep water port," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 378-396.
    13. Wang, Yuhao & Liu, Yongming, 2020. "Bayesian entropy network for fusion of different types of information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
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    Cited by:

    1. Deng, Jian, 2022. "Probabilistic characterization of soil properties based on the maximum entropy method from fractional moments: Model development, case study, and application," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Zhou, Daoqing & Sun, C.P. & Du, Yi-Mu & Guan, Xuefei, 2022. "Degradation and reliability of multi-function systems using the hazard rate matrix and Markovian approximation," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    3. He, Jingran & Gao, Ruofan & Chen, Jianbing, 2022. "A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Jingjing He & Wei Wang & Min Huang & Shaohua Wang & Xuefei Guan, 2021. "Bayesian Inference under Small Sample Sizes Using General Noninformative Priors," Mathematics, MDPI, vol. 9(21), pages 1-20, November.

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