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Reliability modelling for rotorcraft component fatigue life prediction with assumed usage

Published online by Cambridge University Press:  08 July 2016

S. Dekker ,
G. Wurzel  and
R. Alderliesten
S. Dekker*
Affiliation:
Airbus Helicopters Germany, Delft University of Technology, Donauwörth Germany, Marenco Swisshelicopter, Pfäffikon, Zurich, Switzerland
G. Wurzel
Affiliation:
Airbus Helicopters Germany, Donauwörth, Germany
R. Alderliesten
Affiliation:
Delft University of Technology, Delft, The Netherlands
*
sam.dekker@marenco.ch
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Abstract

Fatigue life is a random variable. Thus, the reliability of a conservative fatigue life prediction for a component in the helicopter dynamic system needs to be substantiated. A standard analytical substantiation method uses averaged manoeuvre loads instead of seeing manoeuvre loads as a random variable whose distribution is estimated with limited precision. This simplification may lead to inaccuracies. A new simulation-based method is developed to conservatively predict fatigue life, while also accounting for the full random distribution and uncertainty of manoeuvre loads. Both methods fully account for uncertain fatigue strength but assume that the mission profile is known or can at least be conservatively estimated. Simulations under synthetic but realistic engineering conditions demonstrate that both methods may be used for accurate substantiation of conservative fatigue life predictions. The simulations also demonstrate that, under the tested conditions, uncertainties from manoeuvre loads may be neglected in fatigue life substantiations as the resulting error is not significant with respect to uncertainties in component fatigue strength.

Keywords

Fatigue life predictionservice life limitreliability substantiationhelicopter
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1232 , October 2016 , pp. 1658 - 1692
Copyright
Copyright © Royal Aeronautical Society 2016 

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References

REFERENCES

1. Liard, F. (Ed.). Advisory Group for Aerospace Research & Development, Helicopter Fatigue Design Guide, 1984 NATO AGARD, Structures & Materials Panel, Neuilly sur Seine, France.Google Scholar
2. Schijve, J. Fatigue of Structures and Materials, 2nd ed., 2009, Springer Science+Business Media Berlin, Germany.CrossRefGoogle Scholar
3. Hahn, G.J. and Meeker, W.Q. Statistical Intervals: A Guide for Practitioners, 1991, John Wiley & Sons Inc., US.CrossRefGoogle Scholar
4. Wald, A. and Wolfowitz, J. Tolerance limits for a normal distribution, The Annals of Mathematical Statistics, June 1946, 2, pp 208-215.CrossRefGoogle Scholar
5. Everett, R. A Comparison of Fatigue Life Prediction Methodologies for Rotorcraft, AVSCOM Technical Report 90-B-011, 1990, NASA Langley Research Center, Hampton, Virginia, US.Google Scholar
6. Lombardo, D.C. and Fraser, K.F. Importance of Reliability Assessment to Helicopter Structural Component Fatigue Life Prediction, 2002, DSTO Aeronautical and Maritime Research Laboratory, Fishermans Bend, Victoria, Australia, Technical Note DSTO-TN-0462.Google Scholar
7. Thompson, A.E. and Adams, D.O. A computational method for the determination of structural reliability of helicopter dynamic components, American Helicopter Society Annual Forum, 1990, Washington, D.C., US.Google Scholar
8. Zhao, J. Development and Demonstration of Advanced Structural Reliability Methodologies for Probabilistic Fatigue Damage Accumulation of Aerospace Components, 2008. Available at: https://www.researchgate.net/publication/265756589_Development_and_Demonstration_of_Advanced_Structural_Reliability_Methodologies_for_Probabilistic_Fatigue_Damage_Accumulation_of_Aerospace_Component (accessed 24 June 2016).Google Scholar
9. Zhao, J. and Adams, D.O. Achieving six-nine's reliability using an advanced fatigue relibability assesment model, American Helicopter Society 66th Annual Forum, 2012, Phoenix, Arizona, US.Google Scholar
10. Benton, R.E. Jr. Double-linear cumulative-damage reliability method, American Helicopter Society 67th Annual Forum, 2011, Virginia Beach, Virginia, US.Google Scholar
11. Tang, J. and Zhao, J. A practical approach for predicting fatigue reliability under random cyclic loading, Reliability Engineering and System Safety, June 1995, 50, pp 7-15.CrossRefGoogle Scholar
12. Smith, C.L. and Chang, J.-H. Fatigue reliability analysis of dynamic components with variable loadings without Monte Carlo simulation, American Helicopter Society 63rd Annual Forum, 2007, Virginia Beach, Virginia, US.Google Scholar
13. Moon, S. and Phan, N. Component fatigue life reliability with usage monitor, American Helicopter Society 63rd Annual Forum, 2007, Virginia Beach, Virginia, US.Google Scholar
14. Brown, M.A. and Chang, J.-H. Analytical techniques for helicopter component reliability, American Helicopter Society 64th Annual Forum, 2008, Montreal, Canada.Google Scholar
15. Tong, Y., Antoniou, R. and Wang, C. Probabilistic fatigue life assessment for helicopter dynamic components, Structural Integrity and Fracture International Conference, 2004, Brisbane, Australia.Google Scholar
16. Dekker, S., Bendisch, S. and Hoffmann, F. Fatigue management system and method of operating such a fatigue management system, EP275337 A1, 24 October 2012.Google Scholar
17. Genest, C. and Favre, A.-C. Everything you always wanted to know about copula modeling but were afraid to ask, J. Hydrologic Engineering, July 2007, 12, pp 347-368.CrossRefGoogle Scholar
18. Hurtado, J.E. Structural Reliability - Statistical Learning Perspectives, 1st ed., 2004, Springer-Verlag, Berlin, Heidelberg, Germany.CrossRefGoogle Scholar
19. Lebrun, R. and Dutfoy, A. An innovating analysis of the Nataf transformation from the copula viewpoint, Probabilistic Engineering Mechanics, 2009, 24, pp 312-320.CrossRefGoogle Scholar
20. Echard, B., Gayton, N. and Lemaire, M. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo simulation, Structural Safety, 2011, 33, pp 145-154.CrossRefGoogle Scholar
21. Caron, V., Guyader, A., Zuniga, M.M. and Tuffin, B. Some recent results in rare event estimation, ESAIM Proceedings, January 2014, 44, pp 239-259.CrossRefGoogle Scholar
22. Siu-Kui, A.U. and Beck, J.L. Estimation of small failure probabilities in high dimensions by subset simulation, Probabilistic Engineering Mechanics, 2001, 16, pp 263-277.Google Scholar
23. Hesterberg, T., Moore, D.S., Monaghan, S., Clipson, A. and Epstein, R. Introduction to the Practise of Statistics, 6th ed., 2009, W.H. Freeman.Google Scholar