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Indirect aircraft structural monitoring using artificial neural networks

Published online by Cambridge University Press:  03 February 2016

S. C. Reed
S. C. Reed*
Affiliation:
QinetiQ, Farnborough, UK
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Abstract

From necessity, military aircraft often operate in a highly fatigue damaging environment and history has shown in lost lives and aircraft the consequences of failure to appreciate fully the usage environment. The need for robust and cost effective structural usage monitoring of military aircraft to ensure operations are conducted within acceptable levels of risk is paramount. Furthermore, increased economic pressures require ever-inventive methods to be employed to maximise the lives of military fleets; structural usage monitoring will be a key asset in this drive. A highly cost effective indirect structural health and usage neural network (SHAUNN) monitoring system is proposed. A SHAUNN uses regression relationships determined by artificial neural networks to predict stresses, strains, loads, or fatigue damage from flight parameters. Within this paper the development of a SHAUNN monitoring system is described. Flight parametric data, captured during Operational Loads Measurement of the Royal Air Force Dominie TMk1 aircraft have been used to predict stresses at the key structural location in the wing, using mapping relationships determined by artificial neural networks. A framework for the development of the SHAUNN monitoring system is discussed and the basic architecture of the multilayer perceptron artificial neural network is described. It is concluded that this technology could provide the basis for an accurate, cost-effective structural usage monitoring system and further work to investigate the prediction of ground – based stresses in the wing is recommended.

Type
Research Article
Information
The Aeronautical Journal , Volume 112 , Issue 1131 , May 2008 , pp. 251 - 265
Copyright
Copyright © Royal Aeronautical Society 2008 

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References

1. Hunt, S.R. and Hebden, I.G., Eurofighter 2000: An Integrated Approach to Structural Health and Usage Monitoring, AGARD RTO Proceedings 7, Exploitation of Structural Loads/Health Data for Reduced Life Cycle Costs, Brussels, Belgium, 1998.Google Scholar
2. Azzam, H., A practical approach for the indirect prediction of structural fatigue from measured flight parameters. Proceedings of the Institution of Mechanical Engineers, Part G: J Aerospace Engineering, 211 (G1), pp 29–38, 1997.Google Scholar
3. Azzam, H., Hebden, I., Gill, L., Beaven, F. and Wallace, M., Fusion and decision making techniques for structural prognostic health management, IEEE Aerospace Conference Paper #1535, 2005.Google Scholar
4. Wallace, M., Azzam, H. and New, S., Indirect approaches to individual aircraft structural monitoring, Proceedings of the Institution of Mechanical Engineers, 218, Part G, J Aerospace Engineering, 2004, pp 329–346.Google Scholar
5. Jacobs, J.H. and Perez, P., A combined approach to buffet response analysis and fatigue life prediction, NATO AGARD Report 797 – An assessment of fatigue damage and crack growth prediction techniques, Papers presented at the 77th Meeting of the AGARD Structures and Materials Panel, Bordeaux, France, 29-30 September 1993.Google Scholar
6. Kim, D. and Marciniak, M., A methodology to predict the empennage inflight loads of a general aviation aircraft using backpropagation neural networks, DOT/FAA/AR-00/50, Washington DC, USA, February 2001.Google Scholar
7. Hill, K., Hudson, R.A., Irving, P.E. and Vella, A.D., Loading spectra, usage monitoring and prediction of fatigue damage in helicopters. Proceedings of the 18th ICAF Symposium on Aeronautical Fatigue, May 1995, Melbourne, Australia.Google Scholar
8. Manry, M.T., Hsieh, C.H. and Chandrasekaren, H., Near-optimal flight load synthesis using neural networks. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Madison, Wisconsin, USA, August 1999.Google Scholar
9. Levinski, O., Prediction of buffet loads using artificial neural networks, Document DSTO-RR-0218, Australian Defence Science and Technology Organisation, September 2001.Google Scholar
10. Wang, J., Service loads prediction and recorder data validation using artificial neural networks, PhD Dissertation, Institute of Aeronautics and Astronautics, National Cheng Kung University, Taiwan, July 2003.Google Scholar
11. Reed, S.C. and Cole, D.G., Development of a parametric aircraft fatigue monitoring system using artificial neural networks, in Proceedings of the 22nd Symposium of the International Committee on Aeronautical Fatigue, 2003, Lucerne, Switzerland, ISBN 0954345428, Editor: Guillaume, M. (Ed).Google Scholar
12. Reed, S.C., A parametric-based empennage fatigue monitoring system using artificial neural networks, in Proceedings of the 23rd Symposium of the International Committee on Aeronautical Fatigue, Hamburg, Germany, 2005.Google Scholar
13. Reed, S.C., Development of a parametric-based indirect aircraft structural usage monitoring system using artificial neural networks, Aeronautical J, April 2007, 111, (1118), pp 209–230.Google Scholar
14. Haykin, S., Neural Networks: A Comprehensive Foundation, 2nd Edition, Prentice Hall, 1999, ISBN 0-13-273350-1.Google Scholar
15. Downing, S.D. and Socie, D.F., Simple rainflow counting algorithms, Int J Fatigue, 1982, pp 31–40.Google Scholar
16. ASTM, Standard practices for cycle counting in fatigue analysis, American Society for Testing of Materials, E1049-85 (Re-approved 1997).Google Scholar
17. ESDU, Engineering Sciences Data Unit – Item 89046 – Fatigue of aluminium alloy joints with various fastener systems. Low load transfer, 1989.Google Scholar
18. Goodman, J., Mechanics Applied to Engineering, London, UK, Longmans Green, 1899.Google Scholar
19. Miner, M.A., Cumulative damage in fatigue, J Applied Mechanics, 1945, 12, pp 159–64.Google Scholar
20. Manly, B.F.J., Multivariate Statistical Methods – A Primer, Chapman and Hall CRC Press, Florida, USA, 2nd ed, ISBN 0-412-60300-4, 1994.Google Scholar
21. Tsoukalas, L.H. and Uhrig, R.E., Fuzzy and neural approaches in Engineering, John Wiley and Sons Inc, ISBN 0-471-16003-2, pp 238, 1996.Google Scholar
22. Jepson, B., Collins, A. and Evans, A., Post-Neural Network Procedure to Determine Expected Prediction Values and Their Confidence Limits, Neural Computing and Applications, 1, (3), pp 224–228, Springer-Verlag London Limited, 1993.Google Scholar
23. Duffield, M.J., Revised fatigue index formula for the dominie T1 wing based on finningley operational ‘G’ count records, HST.R.252.FG0352, 1985.Google Scholar
24. Uk, Mod, Ministry of Defence, Joint Service Publication, JSP553, Military Airworthiness Regulations, 1st Ed, 2003.Google Scholar
25. UK MoD Defence Standard 00-970, Design and Airworthiness Requirements for Service Aircraft, Section 3.2.29 – 3.2.55 and Chapter 42 – Structural Monitoring Systems Using Non-Adaptive Prediction Methods, 4, 2006.Google Scholar