Hostname: page-component-7b7567c8c-2n9c9 Total loading time: 0 Render date: 2026-11-14T11:15:36.917Z Has data issue: false hasContentIssue false
  • English
  • Français

Development of a parametric-based indirect aircraft structural usage monitoring system using artificial neural networks

Published online by Cambridge University Press:  03 February 2016

S. C. Reed
S. C. Reed*
Affiliation:
Airworthiness and Structural Integrity Group QinetiQ, Farnborough, UK
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

The development of a parametric-based indirect aircraft structural usage monitoring system using artificial neural networks is described. Flight parametric data, captured during Operational Loads Measurement have been used to predict strains or stresses at key structural locations for several military aircraft types, using mapping relationships determined by artificial neural networks. A framework for the development of a neural network-based structural usage monitor is discussed and the basic architecture of the multilayer perceptron artificial neural network is described. Additionally, results from case studies are presented. It is concluded that this technology could provide the basis for accurate, cost-effective structural usage monitoring systems across the range of military aircraft types and roles.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 111 , Issue 1118 , April 2007, pp. 209 - 230
Copyright
Copyright © Royal Aeronautical Society 2007 

References

1. Gelder, M., Roberts, S., Holford, D. and Reed, S., Fatigue and operational loads monitoring of the Red Arrows, May 2000, Proceedings of the 20th Symposium on Aircraft Integrated Monitoring Systems, Garmisch-Partenkirchen, Germany.Google Scholar
2. Holford, D.M. and Sturgeon, J.R., Operational load measurement: a philosophy and its implementation, 1984, NATO Advisory Group for Aerospace Research and Development, AGARD Conference Proceedings No 375, Sienna, Italy.Google Scholar
3. Ward, A.P., Experience obtained from service fatigue monitoring exercises, 1984, NATO Advisory Group for Aerospace Research and Development, AGARD Conference Proceedings No 375, Sienna, Italy.Google Scholar
4. Reed, S., Holford, D., Gelder, M. and Roberts, S., Operational loads measurement of the Hawk aircraft in RAF service, June 2001, Proceedings of the 21st Symposium of the International Committee on Aeronautical Fatigue, Toulouse, France.Google Scholar
5. Lincoln, J.W., Aging aircraft-USAF experience and actions, 15th Plantema Memorial Lecture June 1997, Proceedings of the 19th Symposium of the International Committee on Aeronautical Fatigue, Edinburgh.Google Scholar
6. Yanishevsky, M., Douchant, A., Breton, M., Turcotte, C. and Sova, M., CT114 Tutor-Snowbird aerobatic aircraft safety by inspection coupon test program, June 2001, Proceedings of the 21st Symposium of the International Committee on Aeronautical Fatigue, Toulouse, France.Google Scholar
7. Hunt, S.R. and Hebden, I.G., Eurofighter 2000: An integrated approach to structural health and usage monitoring, 1998, AGARD RTO Proceedings 7, Exploitation of Structural Loads/Health Data for Reduced Life Cycle Costs, Brussels, Belgium.Google Scholar
8. Azzam, H., A practical approach for the indirect prediction of structural fatigue from measured flight parameters, J Aero Eng, June 1997, 211, (G1), pp 29–38.Google Scholar
9. Azzam, H., Hrbden, I., Gill, L., Beaven, F. and Wallace, M., Fusion and decision making techniques for structural prognostic health management, February 2005, IEEE Aerospace Conference Paper No 1535.Google Scholar
10. Wallace, M., Azzam, H. and Newman, S., Indirect approaches to individual aircraft structural monitoring, J Aero Eng, 2004, 218, (G), pp 329–346.Google Scholar
11. Jacobs, J. H.And Perez, P., A Combined approach to buffet response analysis and fatigue life prediction, An Assessment of Fatigue Damage and Crack Growth Prediction Techniques, 1993, NATO AGARD Report 79, Papers presented at the 77th Meeting of the AGARD Structures and Materials Panel, 29-30 September 1993, Bordeaux, France.Google Scholar
12. Kim, D. and Marciniak, M., A methodology to predict the empennage in-flight loads of a general aviation aircraft using backpropagation neural networks, February 2001, DOT/FAA/AR-00/50, Washington, DC, USA.Google Scholar
13. Hill, K., Hudson, R.A., Irving, P.E. and Vella, A.D., Loading spectra, usage monitoring and prediction of fatigue damage in helicopters, May 1995, Proceedings of the 18th ICAF Symposium on Aeronautical Fatigue, Melbourne, Australia.Google Scholar
14. Manry, M.T., Hsieh, C.H. and Chandrasekaran, H., Near-optimal flight load synthesis using neural networks, August 1999, Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Madison, Wisconsin, USA.Google Scholar
15. Levinski, O., Prediction of buffet loads using artificial neural networks, Document DSTO-RR-0218, September 2001, Australian Defence Science and Technology Organisation.Google Scholar
16. Wang, J., Service Loads Prediction and Recorder Data Validation Using Artificial Neural Networks, July 2003, PhD dissertation, Institute of Aeronautics and Astronautics, National Cheng Kung University, Taiwan.Google Scholar
17. Reed, S. and Cole, D., Development of a parametric aircraft fatigue monitoring system using artificial neural networks, May 2003, Proceedings of the 22nd Symposium of the International Committee on Aeronautical Fatigue, Lucerne, Switzerland.Google Scholar
18. Reed, S., A parametric-based empennage fatigue monitoring system using artificial neural networks, June 2005, Proceedings of the 23rd Symposium of the International Committee on Aeronautical Fatigue, Hamburg, Germany.Google Scholar
19. Gurney, K., An Introduction to Neural Networks, 1997, Routledge, London.Google Scholar
20. Haykin, S., Neural Networks: A Comprehensive Foundation, 1999, Second Edition, Prentice Hall, NJ, USA.Google Scholar
21. Fausett, L.V., Fundamentals of Neural Networks, Architectures, Algorithms and Applications, 1994, Prentice-Hall, NJ, USA.Google Scholar
22. Hagan, M.T., Demuth, H.B. and Beale, M.H., Neural Network Design, 1996, Brooks Cole.Google Scholar
23. Bishop, CM., Neural Networks for Pattern Recognition, 1995, Oxford University Press.Google Scholar
24. Riedmiller, M. and Braun, H., A direct adaptive method for faster backpropagation learning: The RPROP Algorithm, 1993, Proceedings of the IEEE International Conference on Neural Networks.Google Scholar
25. Mackay, D.J.C., Bayesian interpolation, Neural Computing, 1992, 4, (3), pp 416–447.Google Scholar
26. Occam’s Razor (also spelled Ockham’s Razor), is a principle attributed to the 14th-century English logician and Franciscan friar, William of Ockham. It forms the basis of methodological reductionism, also called the principle of parsimony or law of economy. In its simplest form, Occam’s Razor states that one should make no more assumptions than needed, or given two equally predictive theories, choose the simpler.Google Scholar
27. Caron, Y. and Richard, Y., CF-188 Fatigue life management programme, May 1998, 1998 Specialists’ Meeting on Exploitation of Structural Loads/Health Data for Reduced Life Cycle Costs, NATO Research and Technology Organisation Proceedings 7, Brussels, Belgium.Google Scholar
28. ‘No-go’ is a term used in UK military aviation to identify an item of equipment for which the aircraft cannot be launched in peacetime if this equipment is unserviceable.Google Scholar
29. Cronkite, J.D. and Gill, L., Technical evaluation report on 1998 Specialists’ Meeting on Exploitation of Structural Loads/Health Data for Reduced Life Cycle Costs, May 1998, NATO Research and Technology Organisation Proceedings 7, Brussels, Belgium.Google Scholar
30. Tsoukalis, L.H, and Uhrig, R.E., Fuzzy and Neural Approaches in Engineering, 1996, John Wiley and Sons, pp 238.Google Scholar
31. Swingler, K., Applying Neural Networks – A Practical Guide, 1996, Morgan Kaufman Publishers, San Francisco, CA, USA.Google Scholar
32. Mackay, D. J. C., Information Theory, Inference and Learning Algorithms, 2003, pp 527–534, Cambridge University Press.Google Scholar
33. Nabney, I.T., Netlab: Algorithms for Pattern Recognition – Advances in Pattern Recognition, 2002, Springer Publishers.Google Scholar
34. Worden, K., Structural fault detection using a novelty measure, J Sound and Vibration, 1997, 201, (1), pp 85–101.Google Scholar
35. Manly, B.F.J. Multivariate Statistical Methods – A Primer, 1994, Second Edition, Chapman and Hall CRC Press, FL, USA.Google Scholar
36. Staszewski, W.J., Boller, C., Grondell, S., Biemans, C., O Brien, E., Delebarre, C. and Tomlinson, G.R., Damage Detection Using Stress and Ultrasonic Waves: in Health Monitoring of Aerospace Structures – Smart Sensor Technologies and Signal Processing, 2004, pp 179–181, Staszewski, W. J., Boller, C. and Tomlinson, G.R. (Eds), John Wiley and Sons.Google Scholar
37. Military Airworthiness Regulations, July 2003, First Edition, UK Joint Service Publication, JSP553.Google Scholar
38. Reed, S.C., Proposed Defence Standard 00-970 – structural monitoring systems using non-adaptive prediction methods, July 2006, UK Military Aircraft Structural Airworthiness Advisory Group, MASAAG Paper 107A.Google Scholar
39. Design and airworthiness requirements for service aircraft, 2006, Issue 4, UK MoD Defence Standard 00–970.Google Scholar
40. Neuromat, Neural Network Software, June 2001, Version 1.0.Google Scholar
41. Standard practices for cycle counting in fatigue analysis, 1985, American Society for Testing and Materials (ASTM), E1049-85, (Reapproved 1997).Google Scholar
42. Fatigue of aluminium alloy joints with various fastener systems – low load transfer, 1989, Engineering Sciences Data Unit (ESDU), Item 89046.Google Scholar
43. Demuth, H. and Beale, M., Neural Network Toolbox – Mathworks Matlab User’s Guide, 2001, Seventh edition.Google Scholar