Skip to main content
No Access

Assessing structural health of helicopter fuselage panels through artificial neural networks hierarchies

Published Online:pp 216-234

Online assessment of the structural health of aircrafts is crucial both in military and civilian settings. In this paper, Artificial Neural Networks (ANNs) are exploited to obtain a reliable system performing two tasks: diagnosis and prognosis. Diagnosis is devoted to (a) detect a crack, (b) identify the component of the panel involved (bay or stringer) and (c) estimate crack centre and size. Prognosis aims at estimating the evolution of the crack and the Remaining Useful Life (RUL). Training of the ANNs is performed on data sets built through finite elements simulation. Two different ANN hierarchies are presented for diagnosis. Crack evolution is performed for cracks on bay and stringer, separately. Two ANNs are used to estimate the parameters of a crack propagation model (NASGRO equation) for RUL prediction.


structural health monitoring system, artificial neural network, hierarchies of classifiers


  • 1. Bishop, C. (1995). Neural Networks and Pattern Recognition. Oxford:Oxford University Press Google Scholar
  • 2. Chandler, K. , Ferguson, S. , Graver, T. , Csipkes, A. , Mendez, A. (2008). ‘On-line structural health and fire monitoring of a composite personal aircraft using an FBG sensing system’. Proceedings of SPIE – The International Society for Optical Engineering – Smart Sensor Phenomena, Technology, Networks, and Systems 2008. 6933, Google Scholar
  • 3. Fernández-López, A. , Güemes, A. , Gonzalez-Requena, I. , De Miguel-Giraldo, C. , Menéndez, J.M. (2008). ‘Damage detection in composite repair patches using embedded optical sensors’. Proceedings of the 4th European Workshop on Structural Health Monitoring. 818-824 Google Scholar
  • 4. Heredero, R.L. , Frövel, M. , Belenguer, T. , Barrera-Vilar, D. , Quirós, F.G. , Garcia-Olcina, R. , Vague, J. , Sales, S. (2008). ‘Demonstration of the use of Fiber Bragg Grating for optical sensing (FIBOS) during an aerospace mission’. Proceedings of the 4th European Workshop on Structural Health Monitoring. 833-839 Google Scholar
  • 5. Forman, R.G. , Mettu, S.R. , Ernst, H.A. Saxena, A. McDowell, D.L. (1992). ‘Behavior of surface and corner cracks subjected to tensile and bending loads in Ti-6A1-4V alloy’. 1, Fracture Mechanics 22nd Symposium, Philadelphia:American Society for Testing and Materials , 519-546, ASTM STP 1131 Google Scholar
  • 6. Guez, A. , Ahmad, Z. (1988). ‘Solution to the inverse kinematics problem in robotics by neural networks’. Proceedings of IEEE International Conference on Neural Networks. 2, 617-624 Google Scholar
  • 7. Hideki, S. , Shin-Etsu, F. , Tomonaga, O. , Nobuo, T. , Toshimitsu, Y., Jr. (2006). ‘Structural health monitoring of cracked aircraft panels repaired with bonded patches using Fiber Bragg grating sensors’. Applied Composite Materials. 13, 2, 87-98 Google Scholar
  • 8. Hoole, S.R.H. (1993). ‘Artificial neural networks in the solution of inverse electromagnetic field problems’. IEEE Transactions on Magnetics. 29, 2, 1931-1934 Google Scholar
  • 9. Katsikeros, C.E. , Labeas, G.N. (2009). ‘Development and validation of a strain-based structural health monitoring system’. Mechanical Systems and Signal Processing. 23, 372-383 Google Scholar
  • 10. Maniatty, A.M. , Park, E. (2005). ‘Finite element approach to inverse problems in dynamic elastography’. Proceedings of the 5th International Conference on Inverse Problems in Engineering: Theory and Practice. 11–15 July, Cambridge, UK Google Scholar
  • 11. Mitchell, T. (1997). Machine Learning. Boston, MA:McGraw Hill Google Scholar
  • 12. Sbarufatti, C. , Manes, A. , Giglio, M. , Papadrakakis, M. Onate, E. Schrefler, B. (2011a). ‘Advanced stochastic FEM-based artificial neural network for crack damage detection’. Proceedings of 4th International Conference on Computational Methods for Coupled Problems in Sciences and Engineering, Coupled Problems 2011. 20–22 June Google Scholar
  • 13. Sbarufatti, C. , Manes, A. , Giglio, M. , Guedes Soares, C. (2011b). ‘ANN based Bayesian hierarchical model for crack detection and localization over helicopter fuselage panels’. Advances in Safety, Reliability and Risk Management, ESREL 2011. CRC Press, 378-385 Google Scholar
  • 14. Song, J. , Gu, R.J. (2008). ‘A finite element based methodology for inverse problem of determining contact forces using measured displacements’. Proceedings of International Conference on Engineering Optimization (EngOpt’2008). Rio de Janeiro, Brazil Google Scholar
  • 15. Trivailo, P.M. , Dulikravich, G.S. , Sgarioto, D. , Gilbert, T. (2006). ‘Inverse problem of aircraft structural parameter estimation: application of neural networks’. Inverse Problems in Science Engineering. 14, 4, 351-363 Google Scholar
  • 16. Worden, K. , Burrows, A.P. (2001). ‘Optimal sensor placement for fault detection’. Engineering Structures. 23, 885-901 Google Scholar
  • 17. Zienkiewicz, O.C. , Taylor, R.L. , Zhu, J.Z. (2005). The Finite Element Method: Its Basis and Fundamentals. 6th ed., Oxford, UK:Butterworth-Heinemann Google Scholar