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A reliability model of blade to avoid resonance considering multiple fuzziness

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  • Meng Zhang
  • Shan Lu

Abstract

The reliability of aero-engine blade to avoid resonance has a great impact on the safety of airplane. The resonance problem of blade is complex and includes not only randomness but also a lot of fuzziness. By describing the fuzziness of “blade resonance will not occur†through normal bathtub membership function and the inherent frequency through trapezoidal membership function, a Posfust reliability model of blade to avoid resonance, which considers the multiple fuzziness of state and inherent frequency, was proposed based on the probability theory and fuzzy cut-set theory in this article. Meanwhile, three limit forms of the model were analyzed and it was indicated that the Posbist model, Profust model and conventional random model were special cases of the Posfust reliability model in this article. Afterward, analyzing the deficiency of the previous cut-set distributions, a new cut-set distribution named modified truncated normal distribution was proposed to ensure the model has a good convergence in the three special cases. Furthermore, a numerical method was given to solve the Posfust reliability model, and the coefficient of variation of the numerical solution was calculated subsequently. Finally, the model and the numerical method were applied to evaluate the reliability of some blades. Simulations were carried out to explain how one can utilize the model and its numerical method obtained, verify the theoretical analysis results and study the influence of fuzzy degree on the reliability.

Suggested Citation

  • Meng Zhang & Shan Lu, 2014. "A reliability model of blade to avoid resonance considering multiple fuzziness," Journal of Risk and Reliability, , vol. 228(6), pages 641-652, December.
    • Handle: RePEc:sae:risrel:v:228:y:2014:i:6:p:641-652
    DOI: 10.1177/1748006X14543281
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    References listed on IDEAS

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    1. El-Tamaly, Hassan Hussein & Mohammed, Adel A. Elbaset, 2006. "Impact of interconnection photovoltaic/wind system with utility on their reliability using a fuzzy scheme," Renewable Energy, Elsevier, vol. 31(15), pages 2475-2491.
    2. Cai, Baoping & Liu, Yonghong & Liu, Zengkai & Tian, Xiaojie & Dong, Xin & Yu, Shilin, 2012. "Using Bayesian networks in reliability evaluation for subsea blowout preventer control system," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 32-41.
    3. Konstandinidou, Myrto & Nivolianitou, Zoe & Kiranoudis, Chris & Markatos, Nikolaos, 2006. "A fuzzy modeling application of CREAM methodology for human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 706-716.
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    Cited by:

    1. Cheng Lu & Yun-Wen Feng & Cheng-Wei Fei, 2019. "Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis," Energies, MDPI, vol. 12(9), pages 1-16, April.

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