Swallow Neural Network-Empowered High-Speed Brillouin Optical Correlation-Domain Reflectometry: Optimization and Real-Time Operation

In this article, a fiber-optic sensor based on Brillouin optical correlation-domain reflectometry (BOCDR) with a high repetition rate and real-time signal processing function is demonstrated with the assistance of swallow neural networks (NNs) constituted of no more than three hidden layers. In the proposed scheme, the signal processing time for every single Brillouin spectrum is compressed to less than the acquisition period of the spectrum by designing the structure of the NNs, on the basis of the knowledge of the relationship between the implementation time and the preliminary calculation count involved in the NNs. In the experiments with both simulated data and the real-world system, the performances of NNs with different sizes are studied from the perspectives of timing and accuracy. By training NN models with the data acquired from the experiments, real-time dynamic strain measurement is realized with a repetition rate of up to 20 kHz and a dynamic range of
$4000 \mu \varepsilon $
. Different from other works regarding machine learning-empowered measurement acceleration in distributed optical fiber sensors with an offline signal processing phase, the method proposed in this article enables consecutive monitoring of the parameters under test.