Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network
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A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.
KeywordsCarbon fiber reinforced polymer damage identification FBG sensors neural network finite element analysis
This work was supported by the National Natural Science Foundation of China under Grant Nos. 41472260 and 51373090, the Natural Science Foundation of Shandong Province, China under Grant Nos. 2014ZRE27372 and ZR2017BF007, the Fundamental research funds of Shandong University, China under Grant No. 2016JC012, and the Young Scholars Program of Shandong University 2016WLJH30.
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