Fatigue dynamic reliability and global sensitivity analysis of double random vibration system based on Kriging model

References

• Yan Z, Pan X, Lin JH. Double random vibration analysis for coupled Vehicle-Track systems with parameter uncertainties. In: International Conference on vulnerability and risk analysis and management. Liverpool; 2014 July. p. 456–464.
   Google Scholar
• Sofi A, Romeo E. A unified response surface framework for the interval and stochastic finite element analysis of structures with uncertain parameters. Probab Eng Mech. 2018;54:25–36.
   Web of Science ®Google Scholar
• Schenk CA, Pradlwarter HJ, Schueller GI. On the dynamic stochastic response of FE models. Probab Eng Mech. 2004;19(1-2):161–170.
   Web of Science ®Google Scholar
• Wang W, Zhang YM, Li CY. Dynamic reliability nanlysis of linear guides in positioning precision. Mech Mach Theory. 2017;116:451–464.
   Web of Science ®Google Scholar
• Lu C, Feng YW, Liem RP, et al. Improved Kriging with extremum response surface method for structural dynamic reliability and sensitivity analyses. Aerosp Sci Technol. 2018;76:164–175.
   Web of Science ®Google Scholar
• Babykina G, Brinze NI, Aubry JF. Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a stochastic hybrid automaton. Reliab Eng Syst Saf. 2016;152:115–116.
   Web of Science ®Google Scholar
• Chen JB, Li J. The extreme value distribution and dynamic reliability analysis of nonlinear structures with uncertain parameters. Struct Saf. 2007;29(2):77–93.
   Web of Science ®Google Scholar
• Du WQ, Luo YX, Wang YQ. A time-variant performance measure approach for dynamic reliability based design optimization. Appl Math Model. 2019;76:71–86.
   Web of Science ®Google Scholar
• Gao HF, Zio E, Guo JJ, et al. Dynamic probabilistic-based LCF damage assessment of turbine blades regarding time-varying multi-physical field loads. Eng Fail Anal. 2020;108:104193.
   Web of Science ®Google Scholar
• Hu HQ, Huang Y. A dynamic reliability approach to seismic vulnerability analysis of earth dams. Geomech Eng. 2019;18(6):661–668.
   Web of Science ®Google Scholar
• Yang C, Lu ZX, Yang ZY, et al. Parameter identification for structural dynamics based on interval analysis algorithm. Acta Astronaut. 2018;145:131–140.
   Web of Science ®Google Scholar
• Yang C, Lu ZX, Yang ZY. Robust optimal sensor placement for uncertain structures with interval parameters. IEEE Sens J. 2018;18(5):2031–2041.
   Web of Science ®Google Scholar
• Wu YT, Mohanty SJ. Variable screening and ranking using sampling-based sensitivity measures. Reliab Eng Syst Saf. 2006;91:634–647.
   Web of Science ®Google Scholar
• Lu ZZ, Song J, Song SF, et al. Reliability sensitivity by method of moments. Appl Math Model. 2010;34(10):2860–2871.
   Web of Science ®Google Scholar
• Liu J, Tu LW, Liu GZ, et al. An analytical structural global sensitivity analysis method based on direct integral. Inverse Probl Sci Eng. 2019;27(11):1559–1576.
   Web of Science ®Google Scholar
• Helton JC, Davi FJ. Latin hypercube sampling and the propagation of uncertainty in analysis of complex systems. Reliab Eng Syst Saf. 2003;81(1):23–69.
   Web of Science ®Google Scholar
• Helton JC, Johnson JD, Sallaberry CJ, et al. Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliab Eng Syst Saf. 2006;91(10):1175–1209.
   Web of Science ®Google Scholar
• Wei PF, Lu ZZ, Song JW. A new variance-based global sensitivity analysis technique. Comput Phys Comm. 2013;184:2540–2551.
   Web of Science ®Google Scholar
• Wei PF, Lu ZZ, Song JW. Regional and parametric sensitivity analysis of Sobol’s indices. Reliab Eng Syst Saf. 2015;137:87–100.
   Web of Science ®Google Scholar
• Cui L, Lu ZZ, Zhao X. Moment-independent importance measure of basic random variable and its probability density evolution solution. Sci China Tech Sci. 2010;53(4):1138–1145.
   Web of Science ®Google Scholar
• Borgonovo E, Castaings W, Tarantola S. Moment independent importance measures: new results and analytical test cases. Risk Anal. 2011;31(3):404–428.
   PubMed Web of Science ®Google Scholar
• Liu Q, Homma T. A new computational method of a moment-independent uncertainty importance measure. Reliab Eng Syst Saf. 2009;94(7):1205–1211.
   Web of Science ®Google Scholar
• Maschio C, Denis JS. A new methodology for Bayesian history matching using parallel interacting Markov chain Monte Carlo. Inverse Probl Sci Eng. 2018;26(4):498–529.
   Web of Science ®Google Scholar
• Hasofer AM, Lind NC. An exact and invariant first order reliability format. J Eng Mech Div. 1974;100:111–121.
   Web of Science ®Google Scholar
• Zhao YG, Ono T. A general procedure for first/second-order reliability method (FORM/SORM). Struct Saf. 1999;21(2):95–112.
   Web of Science ®Google Scholar
• Meng Z, Zhou HL, Li G, et al. A hybrid sequential approximate programming method for second-order reliability-based design optimization approach. Acta Mech. 2017;228(5):1–14.
   Web of Science ®Google Scholar
• Bichon BJ, Eldred MS, Swiler LP, et al. Efficient global reliability analysis for nonlinear implicit performance functions. AIAA J. 2008;46(10):2459–2468.
   Google Scholar
• Bichon BJ, Mcfarland JM, Welch WJ. Efficient surrogate models for reliability analysis of systems with multiple failure modes. Reliab Eng Syst Saf. 2011;96(10):1386–1395.
   Web of Science ®Google Scholar
• Echard B, Gayton N, Lemarie M. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo simulation. Struct Saf. 2011;33(2):145–154.
   Web of Science ®Google Scholar
• Yang XF, Liu YS, Zhang YS, et al. An active learning Kriging model for hybrid reliability analysis with both random and interval variables. Struct Multidisc Optim. 2015;51(5):1003–1016.
   Web of Science ®Google Scholar
• Yang XF, Liu YS, Zhang YS, et al. Hybrid reliability analysis with both random and probability-box variables. Acta Mech. 2015;226(5):1341–1357.
   Web of Science ®Google Scholar
• Huang X, Chen J, Zhu H. Assessing small failure probabilities by AK-SS: an active learning method combining Kriging and subset simulation. Struct Saf. 2016;59:86–95.
   Web of Science ®Google Scholar
• Zhang YS, Liu YS, Yang XF. Parametric sensitivity analysis for importance measure on failure probability and its efficient Kriging solution. Math Probl Eng. 2015;2015:1–130.
   Web of Science ®Google Scholar
• Dubourg V, Sudret B. Meta-model-based importance sampling for reliability sensitivity analysis. Struct Saf. 2014;49:27–36.
   Web of Science ®Google Scholar
• Song LK, Bai GC, Fei CW. Dynamic surrogate modeling approach for probabilistic creep-fatigue life evaluation of turbine disks. Aerosp Sci Technol. 2019;95:105439.
   Web of Science ®Google Scholar
• Li Y, Barkey LME, Kang HT. Metal fatigue analysis handbook: practical problem-solving techniques for computer-aided engineering. 2011.
   Google Scholar
• Li GQ, Cao H, Li QS, et al. Structural dynamic reliability theory and its application. Beijing: Seismological Press, in Chinese; 1993.
   Google Scholar
• Zhao YG, One T, Idota H. Response uncertainty and time-variant reliability analysis for hysteretic MDF structures. Earthq Eng Struct Dyn. 1999;28:1187–1213.
   Web of Science ®Google Scholar
• Borgonovo E. A new uncertainty importance measure. Reliab Eng Syst Saf. 2007;92(6):771–784.
   Web of Science ®Google Scholar
• Liu QF, Zeng JP, Yang G. MrDIRECT: a multilevel robust DIRECT algorithm for global optimization problems. J Glob Optim. 2015;62(2):205–227.
   Web of Science ®Google Scholar