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Licensed Unlicensed Requires Authentication Published by De Gruyter January 20, 2015

Uncertainty Analysis in Fatigue Life Prediction of Gas Turbine Blades Using Bayesian Inference

  • Yan-Feng Li , Shun-Peng Zhu , Jing Li , Weiwen Peng and Hong-Zhong Huang EMAIL logo

Abstract

This paper investigates Bayesian model selection for fatigue life estimation of gas turbine blades considering model uncertainty and parameter uncertainty. Fatigue life estimation of gas turbine blades is a critical issue for the operation and health management of modern aircraft engines. Since lots of life prediction models have been presented to predict the fatigue life of gas turbine blades, model uncertainty and model selection among these models have consequently become an important issue in the lifecycle management of turbine blades. In this paper, fatigue life estimation is carried out by considering model uncertainty and parameter uncertainty simultaneously. It is formulated as the joint posterior distribution of a fatigue life prediction model and its model parameters using Bayesian inference method. Bayes factor is incorporated to implement the model selection with the quantified model uncertainty. Markov Chain Monte Carlo method is used to facilitate the calculation. A pictorial framework and a step-by-step procedure of the Bayesian inference method for fatigue life estimation considering model uncertainty are presented. Fatigue life estimation of a gas turbine blade is implemented to demonstrate the proposed method.

Acknowledgments

The authors would like to acknowledge the partial supports provided by the National Natural Science Foundation of China under contract number 11272082.

References

1. ZhuSP, HuangHZ, HeLP, LiuY, WangZL. A generalized energy-based fatigue-creep damage parameter for life prediction of turbine disk alloys. Eng Fract Mech2012;90:89100.10.1016/j.engfracmech.2012.04.021Search in Google Scholar

2. ZhangXC, TuST, XuanFZ. Creep-fatigue endurance of 304 stainless steel. Theor Appl Fract Mech2014;71:5166.10.1016/j.tafmec.2014.05.001Search in Google Scholar

3. KeshtgarA, ModarresM. Probabilistic model development for fatigue crack detection using acoustic emission technology. In: Proceedings of International Topical Meeting on Probabilistic Safety Assessment and Analysis, Columbia, SC, USA, 2013.Search in Google Scholar

4. ZhuSP, HuangHZ. A generalized frequency separation-strain energy damage function model for low cycle fatigue-creep life prediction. Fatigue Fract Eng Mater Struct2010;33:22737.10.1111/j.1460-2695.2009.01431.xSearch in Google Scholar

5. HuangHZ, CuiP, PengW, GaoH, WangHK. Fatigue lifetime assessment of aircraft engine disc via multi-source information fusion. Int J Turbo Jet Engines2013;31:16774.10.1515/tjj-2013-0043Search in Google Scholar

6. LeeOS, KimDH, ParkYC. Reliability structures by using probability and fatigue theories. J Mech Sci Technol2008;22:67282.10.1007/s12206-008-0116-3Search in Google Scholar

7. KeshtgarA, ArcariA, IyyerN, KitturM, PhanN. A reliability approach for subcritical crack propagation in high cycle fatigue. In: Proceedings of the 15th Australian International Aerospace Congress, Melbourne, Australia, 2013.Search in Google Scholar

8. HuangHZ, GongJ, ZuoMJ, ZhuSP, LiaoQ. Fatigue life estimation of an aircraft engine under different load spectrums. Int J Turbo Jet Engines2012;29:25967.10.1515/tjj-2012-0017Search in Google Scholar

9. LiuCL, LuZZ, XuYL, YueZF. Reliability analysis for low cycle fatigue life of the aeronautical engine turbine disc structure under random environment. Mater Sci Eng2005;395:21825.10.1016/j.msea.2004.12.014Search in Google Scholar

10. ZhuSP, HuangHZ, OntiverosV, HeLP, ModarresM. Probabilistic low cycle fatigue life prediction using an energy-based damage parameter and accounting for model uncertainty. Int J Damage Mech2012;21:112853.10.1177/1056789511429836Search in Google Scholar

11. SankararamanS, LingY, MahadevanS. Uncertainty quantification and model validation of fatigue crack growth prediction. Eng Fract Mech2011;78:1487504.10.1016/j.engfracmech.2011.02.017Search in Google Scholar

12. LiuY, MahadevanS. Efficient methods for time-dependent fatigue reliability analysis. AIAA J2009;47:494504.10.2514/1.34331Search in Google Scholar

13. SunWY, CuiWM, YuTX. Analysis of the effects of sample size and distribution model on the inference of fatigue life distribution. Mach Des Manuf2009;47:195197.Search in Google Scholar

14. LingD. Research on Weibull distribution and its applications in mechanical reliability engineering. Chengdu: University of Electronic Science and Technology of China, 2010.Search in Google Scholar

15. SiviaDS, SkillingJ. Data analysis: a Bayesian tutorial. Oxford: Oxford University Press, 2006.Search in Google Scholar

16. GuidaM, PentaF. A Bayesian analysis of fatigue data. Struct Saf2010;32:6476.10.1016/j.strusafe.2009.08.001Search in Google Scholar

17. ChenB, BaoR, ZhangJY, FeiBJ. Bayes analysis of fatigue strength distribution parameters. J Beijing Univ Aeronaut Astronaut2003;29:2336.Search in Google Scholar

18. AnD, ChoiJH, KimNH, PattabhiramanS. Fatigue life prediction based on Bayesian approach to incorporate field data into probability model. Struct Eng Mech2011;37:42742.10.12989/sem.2011.37.4.427Search in Google Scholar

19. GuanXF, JhaR, LiuYM. Model selection, updating, and averaging for probabilistic fatigue damage prognosis. Struct Saf2011;33:2429.10.1016/j.strusafe.2011.03.006Search in Google Scholar

20. QianSS, StowCA, BorsukME. On Monte Carlo methods for Bayesian inference. Ecol Model2003;159:26977.10.1016/S0304-3800(02)00299-5Search in Google Scholar

21. LiuJZ, XieLY, XuH. Determining fatigue life reliability model with fuzzy judgment and Bayes theory. Acta Aeronaut Astronaut Sin1994;15:60710.Search in Google Scholar

Received: 2014-12-11
Accepted: 2014-12-28
Published Online: 2015-1-20
Published in Print: 2015-12-1

©2015 by De Gruyter

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