In this article, we are the first to propose deep learning algorithms for intensity wavelength division multiplexing (IWDM)-based self-healing fiber Bragg grating (FBG) sensor network. A deep learning algorithm is proposed to improve the accuracy of measuring the sensing signal of the sensor system. Furthermore, to increase the total number of FBG sensors multiplexed in the sensor network for multipoint measurements, a multiplexing technique called IWDM is proposed. The proposed IWDM-based ring structure FBG sensor network can also have a self-healing purpose to improve the sensor system's reliability and survivability. However, IWDM has unmeasurable gap or crosstalk problems when the number of FBG sensors increases, which causes high sensing signal measurement errors. To solve this problem, a gated recurrent unit (GRU) deep learning algorithm is proposed and experimentally demonstrated. To prove the sensing signal measurement performance of our proposed algorithm, we test the well-trained GRU model using two cases. The first case is when the spectra of FBGs are overlapped as well as the minimum intensity difference between FBGs is 10%, and the second case is when the spectra of FBGs are overlapped as well as the minimum intensity difference between FBGs is 3% which is a very small intensity difference. From the experimental results, the well-trained GRU algorithm achieves high strain sensing signal measurement performance in both cases compared to other algorithms. Therefore, the proposed IWDM based FBG sensor system using deep learning algorithm enhances the multiplexing capacity and survivability of the sensor system, reduces the computational time, and improves strain sensing signal measurement accuracy of FBGs even when FBGs has very small intensity difference and overlap problem.

中文翻译：

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使用IWDM技术和深度学习算法增强FBG传感器系统的复用能力和测量精度

在本文中，我们率先为基于强度波分复用 (IWDM) 的自愈光纤布拉格光栅 (FBG) 传感器网络提出深度学习算法。提出了一种深度学习算法，以提高传感器系统对传感信号的测量精度。此外，为了增加用于多点测量的传感器网络中多路复用的 FBG 传感器总数，提出了一种称为 IWDM 的多路复用技术。所提出的基于IWDM的环形结构FBG传感器网络还可以具有自愈的目的，以提高传感器系统的可靠性和生存能力。然而，当 FBG 传感器数量增加时，IWDM 会出现无法测量的间隙或串扰问题，从而导致较高的传感信号测量误差。为了解决这个问题，提出并实验证明了门控循环单元（GRU）深度学习算法。为了证明我们提出的算法的传感信号测量性能，我们使用两种情况测试训练有素的 GRU 模型。第一种情况是FBG光谱重叠且FBG之间的最小强度差为10％时，第二种情况是FBG光谱重叠且FBG之间的最小强度差为3％时非常小的强度差异。从实验结果来看，与其他算法相比，训练有素的 GRU 算法在两种情况下都实现了高应变传感信号测量性能。因此，提出的基于 IWDM 的 FBG 传感器系统使用深度学习算法增强了传感器系统的复用能力和生存能力，