Jati Mentari Putri, Yao Cheng-Kai, Wu Yen-Chih, Luthfi Muhammad Irfan, Yang Sung-Ho, Dehnaw Amare Mulatie, Peng Peng-Chun
Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Electrical and Electronics Engineering, Vocational Faculty, Universitas Negeri Yogyakarta, Yogyakarta 55281, Indonesia.
Sensors (Basel). 2025 Jun 29;25(13):4047. doi: 10.3390/s25134047.
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.
本研究介绍了一种新颖的深度神经网络（DNN）框架，该框架旨在突破使用光纤布拉格光栅（FBG）传感器结合低功耗、低采样率FBG询问器进行高频振动识别的采样限制。这些询问器虽然节能，但本质上受到采集速率的限制，导致严重的欠采样以及对精确振动分析至关重要的精细光谱细节的模糊。所提出的方法通过仅对原始时域信号进行操作来规避这一限制，从而学会准确识别高频和极其接近的振动分量。使用模拟和实验数据集相结合的广泛验证表明，该模型在广泛的振动频谱上的频率辨别方面具有优越性。这种方法有望在智能光学振动传感以及复杂环境中的紧凑、低功耗状态监测解决方案方面取得重大进展。
