Opt Express. 2023 Feb 27;31(5):8937-8952. doi: 10.1364/OE.479708.
FBG array sensors have been widely used in the multi-point monitoring of large structures due to their excellent optical multiplexing capability. This paper proposes a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN). The stress variations applied to the FBG array sensor are encoded by the array waveguide grating (AWG) as transmitted intensities under different channels and fed to an end-to-end NN model, which receives them and simultaneously establishes a complex nonlinear relationship between the transmitted intensity and the actual wavelength to achieve absolute interrogation of the peak wavelength. In addition, a low-cost data augmentation strategy is introduced to break the data size bottleneck common in data-driven methods so that the NN can still achieve superior performance with small-scale data. In summary, the demodulation system provides an efficient and reliable solution for multi-point monitoring of large structures based on FBG array sensors.
FBG 阵列传感器由于其出色的光学复用能力，已广泛应用于大型结构的多点监测中。本文提出了一种基于神经网络（NN）的 FBG 阵列传感器的经济高效的解调系统。FBG 阵列传感器上施加的应力变化由阵列波导光栅（AWG）编码为不同通道下的传输强度，并馈送到端到端 NN 模型中，该模型接收它们，并同时建立传输强度与实际波长之间的复杂非线性关系，以实现峰值波长的绝对检测。此外，引入了一种低成本的数据增强策略，以打破数据驱动方法中常见的数据大小瓶颈，从而使 NN 仍然可以在小规模数据上实现卓越的性能。总的来说，该解调系统为基于 FBG 阵列传感器的大型结构的多点监测提供了一种高效可靠的解决方案。
