Pattern Recognition for Distributed Optical Fiber Vibration Sensing: A Review
Junchan Li, Yu Wang, Pengfei Wang, Qing Bai, Yan Gao, Hongjuan Zhang, Baoquan Jin
Abstract
In recent years, pattern recognition technologies for distributed optical fiber vibration sensing have attracted more and more attention, aiming to intelligently recognize vibration events along with the optical fiber. Firstly, distributed optical fiber sensors for vibration detection are introduced. Secondly, this paper presents the state of the art of pattern recognition models used in distributed optical fiber vibration sensing systems. The feature extraction method, the model structure, and the processing performance are reported. As the results of the comparison, the support vector machine is a small sample learning method with a solid theoretical foundation and it has excellent generalization ability. The artificial neural network is suitable for massive data learning and multi-classification problems. Also, deep learning can learn more features information by a deep nonlinear network structure in an automated way, and thus has better accuracy and robustness. Furthermore, different applications of pattern recognition for distributed optical fiber vibration sensing are provided, including perimeter security, pipeline monitoring, and railway safety monitoring. Finally, the prospects of pattern recognition for distributed optical fiber vibration sensing are discussed.