Abstract
The paper presents preliminary results of data analysis and discusses the application of soft computing methods in the field of non-destructive tests. The main objective of developed diagnostic system are the automatic detection and evaluation of damage. Thus the system is composed of two signal processing techniques known as novelty detection and pattern recognition. For this purpose autoassociative as well as feed-forward neural networks are used. All the signals used for training the system are obtained from laboratory tests of strip specimens, where phenomenon of elastic wave propagation in solids was utilized. Computed parameters of time signals defines various types of input vectors used for training neural networks. The results finally obtained prove that the proposed diagnostic system made automation of structure testing possible and can be applied to Structural Health Monitoring.
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References
Worden, K., Dulieu-Barton, J.M.: An Overview of Intelligent Fault Detection in Systems and Structures. Structural Health Monitoring 3, 85–98 (2004)
Staszewski, W., Boller, C., Tomlinson, G.: Health Monitoring of Aerospace Structures: Smart Sensor Technologies and Signal Processing. John Wiley & Sons, Chichester (2004)
Waszczyszyn, Z., Ziemiański, L.: Neural networks in the identification analysis of structural mechanics problems. In: Mroz, Z., Stavroulakis, G.E. (eds.) Parameter Identification of Materials and Structures. CISM Courses and Lectures, vol. 469, pp. 259–340. Springer, Heidelberg (2005)
Hernandez-Garcia, M.R., Sanchez-Silva, M.: Learning Machines for Structural Damage Detection. In: Lagaros, N.D., Tsompanakis, Y. (eds.) Intelligent Computational Paradigms in Earthquake Engineering, pp. 158–187. Idea Group Publishing (2007)
Lee, B.C., Staszewski, W.: Lamb wave propagation modeling for damage detection: II. Damage monitoring strategy. Smart Mater. Struct. 16, 260–274 (2007)
Staszewski, W., Lee, B.C., Mallet, L., Scarpa, F.: Structural health monitoring using laser vibrometry: I. Lamb wave sensing. Smart Mater. Struct. 13, 251–260 (2004)
Kuźniar, K., Waszczyszyn, Z.: Neural Networks and Principal Component Analysis for Identification of Building Natural Periods. J. of Comp. in Civil Engineering 20, 431–436 (2006)
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Nazarko, P., Ziemiański, L. (2010). Towards Application of Soft Computing in Structural Health Monitoring. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_8
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DOI: https://doi.org/10.1007/978-3-642-13232-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13231-5
Online ISBN: 978-3-642-13232-2
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