A Deep Learning Framework for Enhancing High-Frequency Optical Fiber Vibration Sensing from Low-Sampling-Rate FBG Interrogators

This study introduces a novel deep neural network (DNN) framework tailored to breaking the sampling limit for high-frequency vibration recognition using fiber Bragg grating (FBG) sensors in conjunction with low-power, low-sampling-rate FBG interrogators. These interrogators, while energy-efficient, are inherently limited by constrained acquisition rates, leading to severe undersampling and the obfuscation of fine spectral details essential for accurate vibration analysis. The proposed method circumvents this limitation by operating solely on raw time-domain signals, learning to recognize high-frequency and extremely close vibrational components accurately. Extensive validation using the combination of simulated and experimental datasets demonstrates the model's superiority in frequency discrimination across a broad vibrational spectrum. This approach is expected to be a significant advancement in intelligent optical vibration sensing and compact, low-power condition monitoring solutions in complex environments.