Chen Shengchao, Yao Feifan, Ren Sufen, Wang Guanjun, Huang Mengxing
Opt Express. 2022 Feb 28;30(5):7647-7663. doi: 10.1364/OE.449004.
Fiber Bragg grating (FBG) sensors have been widely applied in various applications, especially for structural health monitoring. Low cost, wide range, and low error are necessary for an excellent performance FBG sensor signal demodulation system. Yet the improvement of performance is commonly accompanied by costly and complex systems. A high-performance, low-cost wavelength interrogation method for FBG sensors was introduced in this paper. The information from the FBG sensor signal was extracted by the array waveguide grating (AWG) and fed into the proposed cascaded neural network. The proposed network was constructed by cascading a convolutional neural network and a residual backpropagation neural network. We demonstrate that our network yields a vastly significant performance improvement in AWG-based wavelength interrogation over that given by other machine learning models and validate it in experiments. The proposed network cost-effectively widens the wavelength interrogation range of the demodulation system and optimizes the wavelength interrogation error substantially, also making the system scalable.
光纤布拉格光栅（FBG）传感器已广泛应用于各种领域，特别是用于结构健康监测。对于性能优异的FBG传感器信号解调系统而言，低成本、宽量程和低误差是必不可少的。然而，性能的提升通常伴随着昂贵且复杂的系统。本文介绍了一种用于FBG传感器的高性能、低成本波长询问方法。通过阵列波导光栅（AWG）提取FBG传感器信号中的信息，并将其输入到所提出的级联神经网络中。所提出的网络由一个卷积神经网络和一个残差反向传播神经网络级联构成。我们证明，与其他机器学习模型相比，我们的网络在基于AWG的波长询问中实现了显著的性能提升，并通过实验进行了验证。所提出的网络经济高效地拓宽了解调系统的波长询问范围，并大幅优化了波长询问误差，同时还使系统具有可扩展性。
