Recognition and Localization of FBG Temperature Sensing Based on Combined CDAE and 1-DCNN

In quasi-distributed fiber Bragg grating (FBG) temperature sensor networks, noise and spectral distortions affect the demodulation accuracy of the fiber gratings. To address this issue, we construct a sensor network using spectral encoding and propose a novel approach that combines convolutional den...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 24; no. 7; pp. 10125 - 10137
Main Authors: Jiang, Hong, Tang, Rui, Wang, Chenyang, Zhao, Yihan, Li, Hao
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
1-D convolutional neural network (1-DCNN)
Accuracy
Artificial neural networks
Bragg gratings
convolutional denoising autoencoder (CDAE)
Demodulation
fiber Bragg grating (FBG)
Fiber gratings
Goodness of fit
Location awareness
Machine learning
Noise reduction
Optical fiber sensors
Recognition
Reflection
Root-mean-square errors
Sensors
Spectra
spectral distortions
Temperature
temperature sensor networks
Temperature sensors
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:In quasi-distributed fiber Bragg grating (FBG) temperature sensor networks, noise and spectral distortions affect the demodulation accuracy of the fiber gratings. To address this issue, we construct a sensor network using spectral encoding and propose a novel approach that combines convolutional denoising autoencoder (CDAE) and 1-D convolutional neural network (1-DCNN), where CDAE is used for denoising FBG reflection spectra and 1-DCNN is employed for temperature state recognition and temperature demodulation of FBG sensors. The proposed method applies to FBG reflection spectra with different input SNR levels ranging from 0 to 20 dB. Experimental results demonstrate that this CDAE is effective in high-fidelity denoising of the original spectral signals and it outperforms other machine learning techniques. The 1-DCNN model achieves a recognition accuracy of 98.2% for FBG temperature states, with a goodness-of-fit value of 0.9994 for the relationship curve between predicted and actual temperatures, and a root-mean-square error (RMSE) of only 0.3049 °C. This research provides an efficient solution for FBG-based sensor networks.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3365995