Structural failures diagnosis using a hybrid artificial intelligence method / Diagnóstico de falhas estruturais utilizando um método híbrido de inteligência artificial

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

Abstract


This paper presents a Wavelet-artificial immune system algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, is used the wavelet module for transformed the signals into the wavelet domain. Afterward, a negative selection artificial immune system realizes the diagnosis, identifying and classifying the failures. The main application of this methodology is the auxiliary structures inspection process in order to identify and characterize the flaws, as well as perform the decisions aiming at avoiding accidents or disasters. In order to evaluate this methodology, we carried out the modeling and simulation of signals from a numerical model of an aluminum beam, representing an aircraft structure such as a wing. The results demonstrate the robustness and accuracy methodology.


Keywords


Wavelet Artificial Immune Systems (WAIS), Monitoring and Fault Identification, Aeronautical Structures, Artificial Intelligence.

Full Text:

PDF

References


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

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

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

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

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

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

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

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

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

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

B. I. Song, H. Seze, K. A. Giriunas, Collapse Performance Evaluation of Steel Building After Loss of Columns, Proceedings of the Structures Congress (2012), 213–224.

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

F. P A. Lima, F. R. Chavarette, A. S. Souza, S. S. F. Souza, M. Lopes, Artificial Immune Systems with Negative Selection Applied to Health Monitoring of Aeronautical Structures, Advanced Materials Research 871 (2013), 283-289.

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

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

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

L. N. Castro, J. Timmis, Artificial immune systems: a new computational intelligence approach, Springer-Verlag, 2002.

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

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

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

D. Dasgupta, Artificial immune systems and their applications, Springer, 1998.

D. W. Bradley, A. M. Tyrrell, Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation. IEEE Trans. Evolutionary Computation 6 (2002), 227–238.

F. P. A. Lima, C. R. Minussi, R. B. Bessa, J. N. Fidalgo, A modified negative selection algorithm applied in the diagnosis of voltage disturbances in distribution electrical systems. Proc. of 18th International Conference on Intelligent System Application to Power Systems (2015), p. 1-6.

S. Mallat, A Wavelet tour of signal processing, Academic Press, 2 Ed. New York, 637p, 1999.

I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, 1992.

F. P. A. Lima, F. R. Chavarette, S. S. F. Souza, A. S. SOUZA, M. LOPES, Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building, Applied Mechanics and Materials 472 (2014c), 544-549.

F. P. A. Lima, A. D. P. Lotufo, C. R. Minussi, Wavelet-artificial immune system algorithm applied to voltage disturbance diagnosis in electrical distribution systems. IET Generation, Transmission & Distribution 9 (2015), 1104-1111.

MATLAB 7.8 version, MathWorks Company.

L. Roseiro, U. Ramos, R. Leal, Neural Networks in Damage Detection of Composite Laminated Plates, Proc. of the 6th International Conference on Neural Networks (2005), 115-119, 2020.

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.

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/bjdv7n7-116

Refbacks

  • There are currently no refbacks.