Damage diagnosis in an isotropic structure using an artificial immune system algorithm

  • Daniela C. OliveiraEmail author
  • Fábio R. Chavarette
  • Mara L. M. Lopes
Technical Paper


This work proposes a recent methodology for developing structural health monitoring based on intelligent computing techniques, with the purpose of detecting structural damages in aircrafts using artificial immune systems with negative selection. To assess this methodology, an experimental setup was built with piezoelectric transducers attached to an aluminum plate (which represents a wing of an airplane), which work both as actuators and sensors, where signals were acquired in normal and damage situations. The results show robustness and accuracy for the new methodology proposed.


Structural health monitoring Artificial immune systems Negative selection algorithm 



The authors thank the Complex Systems Laboratory (SISPLEXOS) where the project was developed and the financial aid granted by the FAPESP and CNPq.


  1. 1.
    Farrar CR, Lieven NA, Bement MT (2005) An introduction to damage prognosis. In: Inman DJ, Farrar CJ, Lopes Junior V, Steffen Junior V (eds) Damage prognosis for aerospace, civil and mechanical systems. John & Sons, London, pp 1–12Google Scholar
  2. 2.
    Rytter A (1993) Vibration based inspection of civil engineering structures. Thesis (Doctor), Department of Building Technology and Structural Engineering, Aalborg University, DenmarkGoogle Scholar
  3. 3.
    Farrar CR, Worden K (2006) An introduction of structural health monitoring. Philosophical Transactions of the Royal Society A. [S.l.: s.n.]: 203–315Google Scholar
  4. 4.
    Franco VR, Bueno DD, Brennan MJJ, Cavalini JR, Gonsalez CG, Lopes Junior V (2009) Experimental damage location in smart structures using Lamb waves approaches. In: Brazilian conference on dynamics, control and their application—DINCON, Bauru, pp 1–4Google Scholar
  5. 5.
    Haykin S (2008) Neural networks and learning machines, 3rd edn. Prentice-Hall, New YorkGoogle Scholar
  6. 6.
    Jahangiri V, Mirah H, Fathi R, Ettefagh MM (2016) TLP structural health monitoring based on vibrations signal of energy harvesting system. Latin Am J Solids Struct 13(5):897–915CrossRefGoogle Scholar
  7. 7.
    Zadeh LA (1995) Fuzzy sets, information and control. New York 8(3):338–353Google Scholar
  8. 8.
    De Castro LN, Timmis J (2003) Artificial immune systems as a novel soft computing paradigm. Soft Comput J 7:526–544CrossRefGoogle Scholar
  9. 9.
    Zhang J, Hou Z (2014) Application of artificial immune system in structural health monitoring. J Struct. CrossRefGoogle Scholar
  10. 10.
    Anaya M, Tibaduiza DA, Pozo F (2015) A bioinspired methodology based on an artificial system for damage detection in structural health monitoring. Shock Vib 10(40):1–2. CrossRefGoogle Scholar
  11. 11.
    Shi A, Yu X (2017) Structural damage assessment using artificial immune systems and wavelet decomposition. In: International joint conference on neural networks (IJCNN).
  12. 12.
    Sousa SSF, Lima FPA, Chavarette FR (2018) Diagnosis of failures in aeronautical structures using a new approach hybrid based in artificial neural networks and wavelet transform. Int J Pure Appl Math 120:273–282Google Scholar
  13. 13.
    Sousa SSF, Lima FPA, Chavarette FR (2019) Reconhecimento de Falhas Estruturais Utilizando uma Rede Neural Artmap-Fuzzy-Wavelets. Revista Iberoameri-Cana De Ingeniera Mecnica 23, pp. 03–14Google Scholar
  14. 14.
    Tonelli Decanini GMS, Neto MS, Minussi CR (2012) Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory. IET Generation. Transm Distrib 6:1112–1120CrossRefGoogle Scholar
  15. 15.
    Lima FPA, Chavarette FR, Souza SSF, Souza AS, Lopes MLM (2014) Artificial immune systems applied to the analysis os structural integrity of a building. Appl Mech Mater 472:544–549CrossRefGoogle Scholar
  16. 16.
    Ramdane C, Chikhi S (2017) Negative selection algorithm: recent improvements ans its application in intrusion detection system. Int J Comput Acad Res 6(2):20–30Google Scholar
  17. 17.
    Forrest SA, Perelson AL, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of the IEEE symposium on research in security and privacy, 1994, Oakland. Proceedings of the Oakland. IEEE, pp 202–212Google Scholar
  18. 18.
    Jungwon K, Bentley PJ, Aickelin U, Greensmith J, Tedesco G, Twycross J (2007) Immune system approaches to intrusion detection—a review. Nat Comput, Springer, pp 413–466zbMATHGoogle Scholar
  19. 19.
    Lima FPA, Chavarette FR, Souza ASE, Souza SSF, Lopes MLM (2013) Artificial immune systems with negative selection applied to health monitoring of aeronautical structures. Adv Mater Rese Hong King 871:283–289CrossRefGoogle Scholar
  20. 20.
    Bradley DW, Tyrrell AM (2002) Immunotronics—novel finite-state-machine architectures with built-in self-test using self-nonself differentiation. IEEE Trans Evolut Comput New York 6:227–238CrossRefGoogle Scholar
  21. 21.
    Rosa VAM (2016) Localização de danos em estruturas anisotrópicas com a utilização de ondas guiadas. 81 f, 2016. Dissertação (Mestrado Engenharia Mecânica) – Faculdade de Engenharia, Universidade Estadual Paulista, Ilha SolteiraGoogle Scholar
  22. 22.
    Oliveira DC, Lopes Junior V, Fernandes M (2018) Localização de danos com utilização de aprendizado de máquina. Novas Edições Acadêmicas, Mauritius, pp 39–43Google Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  • Daniela C. Oliveira
    • 1
    Email author
  • Fábio R. Chavarette
    • 2
  • Mara L. M. Lopes
    • 2
  1. 1.Department of Mechanical Engineering, Faculty of Engineering of Ilha SolteiraUNESP - Univ.Estadual PaulistaSão PauloBrazil
  2. 2.Department of Mathematics, Faculty of Engineering of Ilha SolteiraUNESP - Univ.Estadual PaulistaIlha SolteiraBrazil

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