Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

被引：57

作者：

Geng, Xiangyi ^{[1
]}

Lu, Shizeng ^{[2
]}

Jiang, Mingshun ^{[1
]}

Sui, Qingmei ^{[1
]}

Lv, Shanshan ^{[1
]}

Xiao, Hang ^{[1
]}

Jia, Yuxi ^{[3
]}

Jia, Lei ^{[1
]}

机构：

[1] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China

[2] Univ Jinan, Sch Elect Engn, Jinan 250022, Shandong, Peoples R China

[3] Shandong Univ, Minist Educ, Key Lab Liquid Solid Struct Evolut & Proc Mat, Jinan 250061, Shandong, Peoples R China

来源：

PHOTONIC SENSORS | 2018年 / 8卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Carbon fiber reinforced polymer; damage identification; FBG sensors; neural network; finite element analysis; COMPOSITE STRUCTURES;

D O I：

10.1007/s13320-018-0466-0

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

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 x 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

引用

页码：168 / 175

页数：8

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