A new approach experimental to diagnosis of the failures in mechanical structures using the artificial immune algorithm with negative selection / Uma nova abordagem experimental para o diagnóstico das falhas nas estruturas mecânicas utilizando o algoritmo de imunidade artificial com seleção negativa

Simone Silva Frutuoso de Souza, Mailon Bruno Pedri de Campos, Fábio Roberto Chavarette, Fernando Parra dos Anjos Lima


In this paper we present a new experimental approach to diagnose failures in mechanical structures using as decision tool an artificial immune algorithm with negative selection. This method is divided into two modules, and the acquisition and data processing module and analysis, detecting and characterizing flaws module. The module for data acquisition and processing of the experimental apparatus is constituted as sensors and actuators, so as to capture the signals in the structure and store it in the computer. From the signal acquisition executed if the negative selection algorithm to identify and characterize flaws in the structure. The main application of this methodology is to assist in the inspection process of mechanical structures in order to identify and characterize the flaws, as well as perform the decisions in order to avoid accidents. To evaluate the proposed methodology, experiments were performed in the laboratory where a real signs database was captured in a structure of the beam type, made of aluminum. The results obtained in the tests show robustness and efficiency when compared to literature.


Failures Diagnosis, Experimental Analysis, Mechanical Structures, Negative Selection Algorithm, Artificial Immune Systems.

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Hall, S. R. (1999). The effective management and use of structural health data, Proceedings of the International Workshop on Structural Health Monitoring, p. 265-275.

Zheng, S., Wang, X., Liu, L. (2004). Damage detection in composite materials based upon the computational mechanics and neural networks, Proc. of the European Workshop on Structural Health Monitoring, Munich, p. 609-615.

Franco, V. R., Bueno, D. D., Brennan, M. J., Cavalini JR., A. A., Gonsalez, C. G., Lopes JR., V. (2009). Experimental damage location in smart structures using Lamb waves approaches. Proc. of the Brazilian Conference on Dynamics, Control and Their Applications, p. 1-4.

Haykin S. (2008). Neural Networks and Learning Machines, Pearson-Pretince-Hall, Third Edition, New York.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, Maryland Heights, v. 8, n. 3, p. 338–353.

de Castro, L. N., Timmis, J. (2002). Artificial Immune Systems: A New Computational Intelligence Approach, Springer. 1st edition.

Krawczuk, M., Ostachowicz, W., Kawiecki, G. (2000). Detection of delamination in cantilevered beams using soft computing methods, Proc. of the Conference on System Identification and Structural Health Monitoring, Madrid, p. 243-252.

Giurgiutiu, V. (2005). Tuned lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring, Journal of Intelligent Material Systems and Structures, vol. 16, n. 4, p. 291-305.

Palaia, L. (2007). Strutural Failure Analysis of timber floors and roofs in ancient buildings at Valencia (Spain). Proc. of the International Conference on Mechanical Behaviour and Failures of the Timber Structures, Florence, p. 1-11.

Chandrashekhar, M., Ganguli, R. (2009). Structural damage detection using modal curvature and fuzzy logic, Structural Health Monitoring, vol. 8, p. 267-282.

Xiang-JUN, C., Zhan-FENG, G., Qiang, G. (2010). Application of wavelet analysis in vibration signal processing of bridge structure. Proc. of the International Conference on Measuring Technology and Mechatronics Automation, p. 671-674.

Shen, T., Wan, F., Song, B., Wu, Y. (2011). Damage Location and Identification of the Wing Structure With probabilistic neural Networks. Proc. of the Prognostics and System Health Management Conference, p. 1-6.

Wang, F. L., Chan, T. H. T., Thambiratnam, D. P., Tan, A. C. C. (2013). Damage Diagnosis for Complex Steel Truss Bridges Using Multi-Layer Genetic Algorithm, Journal of Civil structural Health Monitoring, Springer-Verlag, p. 117-217.

Song, B. I., Seze, H., Giriunas, K. A. (2012). Collapse Performance Evaluation of Steel Building After Loss of Columns, Proc. of the Structures Congress - ASCE (American Society of Civil Engineers), p. 213-224.

Souza, A. S., Chavarette, F. R., Lima, F. P. A., Lopes, M. L. M., Souza, S. S. F. (2013). Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network, Advanced Materials Research, vol. 838-841, p. 3287-3290.

Lima, F. P. A., Chavarette, F. R., Souza, S. S. F., Lopes, M. L. M., Turra, A. E., Lopes Jr., V. (2014). Monitoring and Fault Identification in Aeronautical Structures Using an ARTMAP-Fuzzy-Wavelet Artificial Neural Network, Advanced Materials Research, vol. 1025-1026, p. 1107-1112.

Lima, F. P. A., Chavarette, F. R., Souza, S. S. F., Lopes, M. L. M., Turra, A. E., Lopes Jr., V. (2014). Analysis of Structural Integrity of a Building Using an Artificial Neural Network ARTMAP-Fuzzy-Wavelet, Advanced Materials Research, vol. 1025-1026, p. 1113-1118.

Abreu, C. C. E., Chavarette, F. R., Alvarado, F. V., Duarte, M. A. Q., Lima, F. P. A. (2014). Dual-Tree Complex Wavelet Transform Applied to Fault Monitoring and Identification in Aeronautical Structures, International Journal of Pure and Applied Mathematics, vol. 97, p. 89-97.

Dilena, M.; Limongelli, M. P.; Morassi, A. (2014). Damage Localization in Bridges via the DRD Interpolation Method. Mechanical Systems and Signal Processing. vol. 52-53, p. 162-180.

Dasgupta, D. (1998). Artficial Immune Systems and Their Applications. Springer-Verlag New York, Inc., Secaucus, NJ, USA.

Lima, F. P. A., Lotufo, A. D. P., Minussi, C. R. (2014). Disturbance Detection for Optimal Database Storage in Electrical Distribution Systems Using Artificial Immune Systems with Negative Selection. Electric Power Systems Research, vol. 109, p. 54-62.

Forrest, S., A. Perelson, A. L. & Cherukuri, R. (1994). Self-Nonself Discrimination in a computer, Proc. of IEEE Symposium on Research in Security and Privacy, p. 202-212.

de Castro, L. N. (2001). Immune engineering: development and application of computational tools inspired by artificial immune systems, PhD. Thesis, UNICAMP. (in Portuguese).

Lima, F.P.A.; Lotufo, A.D.P. Minussi, C.R. (2013). Artificial Immune Systems Applied to Voltage Disturbance Diagnosis in Distribution Electrical Systems, Proc. of Powertech, Grenoble, France, Junhe-2013, p. 1-6.

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. vol. 6, p. 227-238.

LabVIEW 2013 version, National Instruments Company.

Sampaio, R. P. C.; Maia, N. M. M.; Silva, J. M. M. (2002). Damage Detection Using the Frequency-Response-Function Curvature Method. Journal of Sound and Vibration. vol. 226, n. 5, p. 1029-1042.

Lee, U.; Shin, J. (2002). A Frequency Response Function Based Structural Damage Identification Method. Computer & Structures. vol. 80, n. 2. p. 117-132.

MATLAB 7.8 version, MathWorks Company.

Roseiro, L., Ramos, U., Leal, R. (2005). Neural Networks in Damage Detection of Composite Laminated Plates, Proc. of the 6th International Conference on Neural Networks, Lisbon, Portugal, p. 115-119.

D. C. Oliveira, F. R. Chavarette, F. P. A. Lima, z Structural Health Monitoring using Artificial Immune System. Brazilian Journal of Developemend. v. 6, n.4, p.16948-16963, 2020.

M. B. P. Campos, G. S. Maciel, S. S. F. Souza, F. R. Chavarette, F. P. A. Lima, Inteligência artificial com aprendizado continuado aplicada ao reconhecimento de padrões. Brazilian Journal of Developemend. v. 6, n. 5, p. 22778-22797, 2020.

DOI: https://doi.org/10.34117/bjdv7n7-083


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