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Belief reliability-based design optimization method with quantile index under epistemic uncertainty
Rui Kang 1,2
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School of Reliability and Systems Engineering, Beihang University, China
Science and Technology on Reliability and Environmental Engineering Laboratory, Beihang University, China
School of Aeronautic Science and Engineering, Beihang University, China
Online publication date: 2023-04-23
Publication date: 2023-04-23
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(2):163545
  • Epistemic uncertainty brings great risk in product reliability design.
  • Uncertainty theory is introduced to quantify epistemic uncertainty.
  • A new belief reliability quantile index is put forward for reliability analysis.
  • Belief reliability-based design optimization methods are proposed using the quantile index.
  • The proposed method shows good accuracy and efficiency.
Product reliability design optimization is affected by epistemic uncertainty greatly, which leaves significant risks in product use. In this paper, a new belief reliability-based design optimization (BRBDO) method under epistemic uncertainty is established to handle this problem. First, the belief reliability theory is introduced into the design optimization problem, and a quantile index is proposed to quantify belief reliability level based on uncertainty theory, through which a rapid analysis method called first order belief reliability analysis (FOBRA) method is developed. Then, according to the different trade-off strategies, two types of design optimization models are established, and corresponding FOBRA-based computation methods are also demonstrated. Finally, several case applications are studied to verify the effectiveness of the analysis and design optimization methods proposed in this paper. The results indicate that the BRODO method with the quantile index can save a lot of computational time with acceptable accuracy and can rationally cope with epistemic uncertainty.
This work was supported by National Natural Science Foundation of China [Grant No. 62073009], Stable Supporting Project of Science and Technology on Reliability and Environmental Engineering Laboratory [Grant No. WDZC20220102], Funding of Science and Technology on Reliability and Environmental Engineering Laboratory [Grant No. 6142004210102], and China Postdoctoral Science Foundation [Grant No. 2022M710314].
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