Ms. Zihan Cao Profile

Zihan Cao

at Sun Yat-Sen Univ

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Proceedings Article | 26 July 2022 Paper

Wavelength detection of FBG temperature sensor based on deep neural networks

Zihan Cao, Shengqi Zhang, Zhengyong Liu, Yichang Wu, Chengkun Yang, Zhaohui Li

Proceedings Volume 12279, 122790D (2022) https://doi.org/10.1117/12.2616366

KEYWORDS: Fiber Bragg gratings, Temperature metrology, Sensors, Data modeling, Temperature sensors, Neural networks, Demodulation, Convolutional neural networks, Calibration

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Fiber Bragg grating (FBG) sensor has been one of the main research objects in the field of optical sensing due to its advantages of compact size and capability of multiplexing and long-distance measurement. Since its central wavelength shift has a linear relationship with the measurand change, the typical demodulation method is to fit such relationship linearly or nonlinearly, and demodulate the measured parameter based on the change of Bragg wavelength. In this paper, particularly for an FBG temperature sensor, an effective sensing signal measurement method employing deep convolutional neural networks (DCNN) is proposed. This method can extract temperature automatically with a comparable and even more accurate precision. After training process of DCNN, the temperature information can be directly extracted from the experimentally obtained FBG spectra instead of tracking the peak. Since it makes full use of the spectral information rather than only the central wavelength, it overcomes the limit of traditional fitting method and can improve the measurement accuracy of FBG effectively, with an accuracy of 99.38% and mean error of 0.608. The proposed approach to demodulate the FBG sensors is experimentally verified by checking various spectra obtained at different temperatures, and superior accuracy could be achieved. It provides a cost-effective solution for multiplexing of FBG sensors, and is promising for establishing sensing networks to implement smart monitoring.

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