Abstract

In this paper, a structural damage identification algorithm based on a single fiber Bragg grating (FBG) sensor is proposed. The signal detected by the FBG can be analyzed by the wavelet packet decomposition and back propagation neural network to obtain the damage location information. A high-speed FBG demodulation system based on a tunable Fabry–Perot filter and unbalanced Mach–Zehnder (M-Z) interferometer is designed to respond to a signal with a frequency range from 0 to 4 kHz, which will increase the sensing accuracy. This algorithm is verified by the aluminum plate model, which can simulate the generation of damage in reality. The experimental results show that a single FBG sensor is enough to realize accurate damage location identification according to this algorithm, and identification accuracy can reach 90.0%.

© 2019 Optical Society of America

Damage identification using deep learning and long-gauge fiber Bragg grating sensors

Wenda Zhang and Dapeng Wang
Appl. Opt. 59(33) 10532-10540 (2020)

Wavelength detection of model-sharing fiber Bragg grating sensor networks using long short-term memory neural network

Hao Jiang, Qiying Zeng, Jing Chen, Xiaojie Qiu, Xinyu Liu, Zhenghua Chen, and Xiren Miao
Opt. Express 27(15) 20583-20596 (2019)

Wavelet packet energy characterization of low velocity impacts and load localization by optical fiber Bragg grating sensor technique

Jiyun Lu, Bangfeng Wang, and Dakai Liang
Appl. Opt. 52(11) 2346-2352 (2013)

High-precision demodulation method for the cobweb FBG sensor network

Hong Jiang, Chenyang Wang, Yihan Zhao, and Rui Tang
Appl. Opt. 62(2) 419-428 (2023)

Fiber Bragg grating dynamic strain sensor using an adaptive reflective semiconductor optical amplifier source

Heming Wei, Chuanyi Tao, Yinian Zhu, and Sridhar Krishnaswamy
Appl. Opt. 55(10) 2752-2759 (2016)