The higher costs related to maintenance processes in structures such as aircraft, coupled with the growing demand of them, fueling the need for investment in innovative techniques for structural monitoring. Thus, the work done in this thesis seeks to develop a technique of active monitoring, aiming at monitoring of structure parameters analyzed in order to identify and characterize processes of hidden damage such as corrosion and delamination. The maid methodology was based on the analysis of patterns of surface deformation, obtained with the use of nets of optical fiber sensors based on fiber Bragg gratings ( FBG ). Initially, these patterns were caused by static loads (tension ), and later by PTZ actuators fixed to the frame, who are subjected to an AC voltage and fixed frequency. This technique has all the advantages of the FBG s sensors (mass and small dimensions, electromagnetic immunity, high multiplexing s power and high sensitivity among others), in addition to allowing visualization of changes in the patterns of deformation caused by damage, by varying the frequency excitation. With respect to the interpretation of the results, the strategy employed was to separate the problem of detection and characterization of damage. Thus, the detection is performed by comparing the deformation energy of the surface of the specimens in the cases with and without defect, whereas the characterization is obtained through the use of artificial neural networks (ANN) by means of pattern recognition routines.