Shi Yi, Wang Yuanye, Zhao Lei, Fan Zhun
Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, School of Engineering, Shantou University, Shantou 515063, China.
Sensors (Basel). 2019 Aug 4;19(15):3421. doi: 10.3390/s19153421.
Phase-sensitive optical time domain reflectometer (Φ-OTDR) based distributed optical fiber sensing system has been widely used in many fields such as long range pipeline pre-warning, perimeter security and structure health monitoring. However, the lack of event recognition ability is always being the bottleneck of Φ-OTDR in field application. An event recognition method based on deep learning is proposed in this paper. This method directly uses the temporal-spatial data matrix from Φ-OTDR as the input of a convolutional neural network (CNN). Only a simple bandpass filtering and a gray scale transformation are needed as the pre-processing, which achieves real-time. Besides, an optimized network structure with small size, high training speed and high classification accuracy is built. Experiment results based on 5644 events samples show that this network can achieve 96.67% classification accuracy in recognition of 5 kinds of events and the retraining time is only 7 min for a new sensing setup.
基于相敏光时域反射仪（Φ-OTDR）的分布式光纤传感系统已广泛应用于长距离管道预警、周界安全和结构健康监测等诸多领域。然而，缺乏事件识别能力一直是Φ-OTDR在现场应用中的瓶颈。本文提出了一种基于深度学习的事件识别方法。该方法直接将来自Φ-OTDR的时空数据矩阵作为卷积神经网络（CNN）的输入。预处理仅需简单的带通滤波和灰度变换，实现了实时性。此外，构建了一种尺寸小、训练速度快、分类精度高的优化网络结构。基于5644个事件样本的实验结果表明，该网络在识别5种事件时的分类准确率可达96.67%，对于新的传感设置，重新训练时间仅为7分钟。
