CFRP damage identification system by using FBG sensor and RBF neural network

Abstract

A damage identification system of carbon fiber reinforced plastics (CFRP) structures was studied using the damage detection network, which was constituted by fiber Bragg grating (FBG) sensors, and radial basis function (RBF) neural network. First, FBG sensors were used to detect the structural dynamic response signals, which were excited by active excitation method. Then, the damage characteristic was extracted by Fourier transform from the signal. In addition, the RBF neural network was designed to identify the type of damage, with the damage characteristic as the input and the damage state as the output. At last, the system of CFRP by using FBG sensors was verified by experimental method. The results showed that, in the 160mm*160mm experimental area of CFRP plate, the damage state was investigated with accurate identification. Briefly, the system provided an effective method for CFRP structural damage identification.