# Monitoring and Fault Identification in Aeronautical Structures Using an Wavelet-Artificial Immune System Algorithm

## Abstract

This chapter presents a Wavelet-Artificial Immune System (WAIS) algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, the wavelet module is used to transform the signals into the wavelet domain. Afterward, a negative selection artificial immune system performs the diagnosis via identifying and classifying the failures. The main application of this methodology is in the auxiliary structures inspection process in order to identify and characterize the flaws as well as assist in the decision making process that is 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 that represent an aircraft structure such as a wing. The proposed algorithm presented good results, with 100% matching in detecting and classifying of the failures tested. The results demonstrate the robustness and accuracy of the methodology.

## Keywords

Wavelet-artificial immune systems (WAIS) Monitoring and fault identification Aeronautical structures Artificial intelligence## References

- 1.S.R. Hall, The effective management and use of structural health data, in
*Proceedings of the International Workshop on Structural Health Monitoring*(1999), pp. 265–275Google Scholar - 2.S. Zheng, X. Wang, L. Liu, Damage detection in composite materials based upon the computational mechanics and neural networks, in
*Proceedings of the European Workshop on Structural Health Monitoring*(2004), pp. 609–615Google Scholar - 3.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, in
*Proceedings of the Brazilian Conference on Dynamics, Control and Their Application*(2009), pp. 1–4Google Scholar - 4.M. Krawczuk, W. Ostachowicz, G. Kawiecki, Detection of delamination in cantilevered beams using soft computing methods, in
*Proceedings of the Conference on System Identification and Structural Health Monitoring*, Madrid (2000), pp. 243–252Google Scholar - 5.V. Giurgiutiu, Tuned lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring. J. Intell. Mater. Syst. Struct.
**16**, 291–305 (2005)CrossRefGoogle Scholar - 6.L. Palaia, Structural Failure Analysis of timber floors and roofs in ancient buildings at Valencia (Spain), in
*Proceedings of the International Conference on Mechanical Behavior and Failures of the Timber Structures*(2007), pp. 1–11Google Scholar - 7.M. Chandrashekhar, R. Ganguli, Structural damage detection using modal curvature and fuzzy logic. Struct. Health Monit.
**8**, 267–282 (2009)CrossRefGoogle Scholar - 8.X.J. Chen, Z.-F. Gao, Y.-E. Ma, Q. Guo, Application of wavelet analysis in vibration signal processing of bridge structure, in
*Proceedings of the International Conference on Measuring Technology and Mechatronics Automation*(2010), pp. 671–674Google Scholar - 9.T. Shen, F. Wan, B. Song, Y. Wu, Damage location and identification of the wing structure with probabilistic neural networks, in
*Proceedings of the Prognostics and System Health Management Conference*(2011), pp. 1–6Google Scholar - 10.F.L. Wang, T.H. Chan, D.P. Thambiratnam, A.C. Tan, Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm. J. Civil Struct. Health Monit.
**3**(2), 117–217 (2013)CrossRefGoogle Scholar - 11.B.I. Song, H. Seze, K.A. Giriunas, Collapse performance evaluation of steel building after loss of columns, in
*Proceedings of the Structures Congress*(2012), pp. 213–224Google Scholar - 12.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. Adv. Mater. Res.
**838–841**, 3287–3290 (2013)CrossRefGoogle Scholar - 13.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. Adv. Mater. Res.
**871**, 283–289 (2013)CrossRefGoogle Scholar - 14.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. Adv. Mater. Res.
**1025–1026**, 1113–1118 (2014)CrossRefGoogle Scholar - 15.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. Adv. Mater. Res.
**1025–1026**, 1107–1112 (2014)CrossRefGoogle Scholar - 16.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. Int. J. Pure Appl. Math.
**97**, 89–97 (2014)CrossRefGoogle Scholar - 17.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. Electr. Power Syst. Res.
**109**, 54–62 (2014)CrossRefGoogle Scholar - 18.S. Forrest, A. Perelson, L. Allen, R. Cherukuri, Self-nonself discrimination in a computer, in
*Proceedings of IEEE Symposium on Research in Security and Privacy*(1994), pp. 202–212Google Scholar - 19.L.N. Castro, J. Timmis,
*Artificial Immune Systems: A New Computational Intelligence Approach*(Springer, 2002)Google Scholar - 20.L.N. Castro, Immune engineering: development and application of computational tools inspired by artificial immune systems. Ph.D. thesis, UNICAMP, 2001 (in Portuguese)Google Scholar
- 21.D. Dasgupta,
*Artificial Immune Systems and Their Applications*(Springer, 1998)Google Scholar - 22.D.W. Bradley, A.M. Tyrrell, Immunotronics—novel finite-state-machine architectures with built-in self-test using self-nonself differentiation. IEEE Trans. Evol. Comput.
**6**, 227–238 (2002)CrossRefGoogle Scholar - 23.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, in
*Proceedings of 18th International Conference on Intelligent System Application to Power Systems*(2015), pp. 1–6Google Scholar - 24.S. Mallat,
*A Wavelet Tour of Signal Processing*, 2 edn. (Academic Press, New York, 1999), 637 pp.Google Scholar - 25.I. Daubechies,
*Ten Lectures on Wavelets*(Society for Industrial and Applied Mathematics, 1992)Google Scholar - 26.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. Appl. Mech. Mater.
**472**, 544–549 (2014)CrossRefGoogle Scholar - 27.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 Gener. Transm. Distrib.
**9**, 1104–1111 (2015)CrossRefGoogle Scholar - 28.MATLAB 7.8 version, MathWorks CompanyGoogle Scholar
- 29.L. Roseiro, U. Ramos, R. Leal, Neural networks in damage detection of composite laminated plates, in
*Proceedings of the 6th International Conference on Neural Networks*(2005), pp. 115–119Google Scholar