Abstract
The distributed optical fiber sensing technology based on phase-sensitive optical time-domain reflectometer (Φ-OTDR) has the advantage of multi-point vibration monitoring simultaneously. To improve the robustness of the recognition system, a deep learning model is designed to extract time-frequency sequence correlation from signals and spectrograms. This lightweight plug-and-play convolutional neural network architecture achieves better performance with a lower number of extra parameters and computations. This study is verified on a vibration dataset containing eight different scenarios collected by a Φ-OTDR system, and the classification accuracy of 96.02% is achieved.
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