CLASSIFICAÇÃO DE DANOS POR MEIO DE MAPAS AUTO-ORGANIZÁVEIS (SOM) ASSOCIADO AO MONITORAMENTO DA INTEGRIDADE ESTRUTURAL BASEADO NA IMPEDÂNCIA ELETROMECÂNICA

Abstract

Structural Health Monitoring (SHM) is a very cost-effective technique to reduce mainte- nance costs, increase life-cycle, and improve the performance ofengineering structures. The impedance-based methodology uses the electromechanical behavior of piezoelectric mate- rials (PZTs) to detect structural damages. This technique uses high frequencies and excites the local modes, thus providing the monitoring of any change of the structural mechanical impedance in the region ofinfluence ofPZT patch. From the variation of the impedance sig- nals, it can be concluded whether or not there is a damage. Artificial neural networks (RNA) are part ofa broad concept called artificial systems. The foundation ofneural networks is as- sociated with the functioning of the human brain, which after training has the ability to per- formassociations. This science has great applicability in the solution ofartificial intelligence problems, through the modeling of systems that use connections that make it possible to si- mulate the human nervous system. This work uses Kohonen’s self-organizing maps (SOM) associated to SHM based on electromechanical impedance for the detection and classifica- tion ofdamages in an aluminumbeam. Based on the system under analysis, the network was trained to five different failure and severity positions. Through the neural network model of self-organizing maps, the network provided 30 maps as answers to the training and learning process. With this, it was realized qualitatively based on the concentration of energy of the maps that the grouping and classification ofthe different conditions ofdamages in which the engineering structure was submitted, happened with success. In order to establish a quan- titative analysis proving the potential of the SOM network, the Hamming distance formula was applied, in which the results confirmed its accuracy.