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A probabilistic-based airframe integrity management model

Author

Listed:
  • Wang, X.
  • Rabiei, M.
  • Hurtado, J.
  • Modarres, M.
  • Hoffman, P.

Abstract

This paper proposes a lognormal distribution model to relate crack-length distribution to fatigue damage accumulated in aging airframes. The fatigue damage is expressed as fatigue life expended (FLE) and is calculated using the strain-life method and Miner's rule. A two-stage Bayesian updating procedure is constructed to determine the unknown parameters in the proposed semi-empirical model of crack length versus FLE. At the first stage of the Bayesian updating, the crack closure model is used to simulate the crack growth based upon generic but uncertain physical properties. The simulated crack-growth results are then used as data to update the uninformative prior distributions of the unknown parameters of the proposed semi-empirical model. At the second stage of the Bayesian updating, the crack-length data collected from field inspections are used as evidence to further update the posteriors from the first stage of the Bayesian updating. Two approaches are proposed to build the crack-length distribution for the fleet based on individual posterior crack distribution of each aircraft. The proposed distribution model of the crack length can be used to analyze the reliability of aging airframes by predicting, for instance, the probability that a crack will reach an unacceptable length after additional flight hours.

Suggested Citation

  • Wang, X. & Rabiei, M. & Hurtado, J. & Modarres, M. & Hoffman, P., 2009. "A probabilistic-based airframe integrity management model," Reliability Engineering and System Safety, Elsevier, vol. 94(5), pages 932-941.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:5:p:932-941
    DOI: 10.1016/j.ress.2008.10.010
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    Citations

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    Cited by:

    1. Soliman, Ahmed A. & Abd-Ellah, Ahmed H. & Abou-Elheggag, Naser A. & Ahmed, Essam A., 2012. "Modified Weibull model: A Bayes study using MCMC approach based on progressive censoring data," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 48-57.
    2. Rabiei, Masoud & Modarres, Mohammad, 2013. "A recursive Bayesian framework for structural health management using online monitoring and periodic inspections," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 154-164.
    3. 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.
    4. Baraldi, Piero & Mangili, Francesca & Zio, Enrico, 2013. "Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 94-108.
    5. Samarakoon, Samindi M.K. & Ratnayake, R.M. Chandima, 2015. "Strengthening, modification and repair techniques’ prioritization for structural integrity control of ageing offshore structures," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 15-26.
    6. An, Dawn & Choi, Joo-Ho & Kim, Nam Ho, 2013. "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 161-169.
    7. Likun Ren & Weimin Lv & Shiwei Jiang, 2018. "Machine prognostics based on sparse representation model," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 277-285, February.
    8. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.

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