Structural Health Monitoring using Artificial Immune System / Monitoramento estrutural da saúde usando sistema imunológico artificial

Daniela Cabral de Oliveira, Fábio Roberto Chavarette, Fernando Parra dos Anjos Lima


This work proposes intelligent computer techniques which aims to detect structural damages in aircraft using the artificial immune system technique with negative selection and clonal selection. This concept results in a diagnosis system that is able to learn continuously, comprising different damage situations, without needing to restart the learning process. Considering this, two artificial immune systems were employed, the first of which, the negative selection algorithm, is responsible for pattern recognition, and second one, the clonal selection algorithm, is responsible for the continuous learning process. The experiment was prepared using piezoelectric transducers attached to an aluminum plate (representing an airplane wing), which act both as sensors and actuators, and signals that represent baseline and damage conditions were acquired. The results show that the proposed methodology is robust and accurate.



Structural health monitoring, Isotropic structures, Artificial immune systems, Negative selection algorithm, Clonal selection algorithm.

Full Text:



Bradley, D. W., & Tyrrell, A. M. (2002). Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation. IEEE Transactions on Evolutionary Computation, New York, v. 6, p. 227-238.

Bueno, D. D., Marqui, C. R., Lopes Junior, V., Brennan, M. J., Inman, D. J. (2012). Structural Damage Identification and Location Using Grammian Matrices. Shock and Vibration, v. 19, p. 287- 299.

De Castro, L. N., & Von Zuben, F. J. (2000). The clonal selection algorithm with engineering applications. In: Workshop Proceedings of Gecco, Workshop on Artificial Immune Systems and Their Applications. Las Vegas. Proceedings [...] Las Vegas: [s. n.]. p. 36-39.

De Castro, L. N. (2001). Engenharia imunológica: desenvolvimento e aplicação de ferramentas computacionais inspiradas em sistemas imunológicos artificiais. 286 f. Tese (Doutorado) - Faculdade de Engenharia Elétrica e de Computação, Universidade Estadual de Campinas, Campinas.

De Castro, L. N., & Timmis, J. (2002). Artificial immune systems: a new computational intelligence approach. New York: Springer. p.357.

De Castro, L. N., & Timmis, J. (2003). Artificial Immune Systems as a Novel Soft Computing Paradigm. Soft Computing Journal. p. 526- 544.

De França, F. O. (2005). Algoritmos bio-inspirados aplicados à otimização dinâmica. 90 f. Dissertação (Mestrado) - Faculdade de Engenharia Elétrica e de Computação, Universidade Estadual de Campinas, Campinas.

Doebling, S. W., Farrar, C. R., Prime, M. B. (1998). A summary review of vibration-based damage identification methods. The Shock and Vibration Digest, Thousand Oaks, v. 30, n. 2, p. 91-105.

Farrar, C. R., Lieven, N. A., Bement, M. T. (2005). An introduction to damage prognosis. In: D. J. Inman; C.J. Farrar, V. Lopes Junior, V. Steffen Junior, Damage prognosis for aerospace, civil and mechanical systems. England: John & Sons, p. 1-12.

Farrar, C. R., & Worden, K. (2006). An introduction of structural health monitoring: philosophical transactions of the royal society A. [S.l.: s.n.], p. 203-315.

Franco, V. R., Bueno, D. D., Brennan, M. J., Cavalini, J. R., Gonsalez, C. G., 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, p. 1- 4.

Forrest, S. A. A., Perelson, L., 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, p. 202-212.

Hall, S. R. (1999). The effective management and use of structural health data. In. International Workshop on Structural Health Monitoring, 2. New York. Proceedings […] New York: Virginia Tech Publisher, p. 265-275.

Haykin, S. (2008). Neural networks and learning machines. 3° ed. New York: Prentice-Hall, 936 p.

Jungwon, K., Bentley, P. J., Aickelin, U., Greensmith, J., Tedesco, G., Twycross, J. (2007). Immune system approaches to intrusion detection – a review. Nature Computing, Springer, p. 413-466.

Lima, F. P. A., Chavarette, F. R., Souza, A. S. E., Souza, S. S. F., Lopes, M. L. M. (2013). Artificial immune systems with negative selection applied to health monitoring of aeronautical structures. Advanced Materials Research, Hong King, v.871, s/n, p.283-289.

Lima, F. P. A. (2014). Monitoramento e identificação de falhas em estruturas aeronáuticas e mecânicas utilizando técnicas de computação inteligente. 72 f. Dissertação (Mestre em Engenharia Mecânica) – Faculdade de Engenharia, Universidade Estadual Paulista – UNESP, Ilha Solteira.

Lima, F. P. A. (2016). Diagnóstico de distúrbios de tensão em sistemas de distribuição baseado num sistema imunológico artificial com aprendizado continuado. 101 f. Tese (Doutorado Engenharia Elétrica) – Faculdade de Engenharia, Universidade Estadual Paulista, Ilha Solteira.

Lima, F. P. A., Chavarette, F. R., Souza, S. S. F. (2018). Diagnosis of Failures in Aeronautical Structures Using a New Approach Hybrid Based in Artificial Neural Networks and Wavelet Transform. International Journal of Pure and Applied Mathematics, Sofia, v. 120, p. 273-282.

Lopes Junior, V., Gyuhae, P., Harley, H. C., Daniel, J. I (2000). Impedance based structural health monitoring with artificial neural networks. Journal of Intelligence Material Systems and Structures, England, v. 4, n. 15, p. 45.

Morales, J. D. V. (2009). Detecção de dano em estruturas utilizando algoritmos genéticos e parâmetros dinâmicos. 191 f. Dissertação (Mestrado em Engenharia de Estruturas) – Escola de Engenharia de São Carlos, Universidade de São Paulo – USP, São Carlos.

Oliveira, D. C. (2017). Localização de danos em estruturas isotrópicas com a utilização de aprendizado de máquina. 125 f. Dissertação (Mestrado Engenharia Mecânica) – Faculdade de Engenharia, Universidade Estadual Paulista, Ilha Solteira.

Oliveira, D. C; Chavarette F. R, Lopes M. L. M (2019). Damage Diagnosis in na Isotropic Structure Using an Artificial Immune System Algorithm. Journal of the Brazilian Society of Mechanical Sciences and Engineering, DOI 10.1007/s40430-019-1971-9, 2019.

Ramdane, C., & Chikhi, S. (2017). Negative selection algorithm: recent improvements ans its application in intrusion detection system. International Journal of Computing Academic Research, v.6, n. 2, ISSN 2305-9184 p. 20-30.

Rosa, V. A. M. (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 Solteira.

Souza, S. F. S., Lima, P. A. F., Chavarette, R. F. (2015). Monitoring of structural integrity using unsupervised data clustering techniques. International Journal of Pure and Applied Mathematics, Cambridge, v. 104, n. 17, p. 119-133.

Wylie, C. S., & Shakhnovich, E. I. (2012). Mutation induced extinction infinite populations: lethal mutagenesis and lethal isolation. PLoS Computational Biology, New York, v. 8, p. 1-6.

Zadeh, L. A. (1995). Fuzzy sets, Information and Control. New York, v. 8, n. 3, p. 338-353.

Zhongqing, S., Wang, X., Yu, L. C. L., Chen, Z. (2009). On selection of data fusion schemes for structural damage evaluation. Structural Health Monitoring, Cambridge, v. 8, n. 3, p. 223-241.



  • There are currently no refbacks.