Pattern recognition based on pulse scanning imaging and convolutional neural network for vibrational events in Φ-OTDR
Pattern recognition based on pulse scanning imaging and convolutional neural network for vibrational events in Φ-OTDR
复制标题
基于脉冲扫描成像和卷积神经网络的δ-OTDR振动事件模式识别
DOI：
10.1016/j.ijleo.2020.165205
复制
发表时间：
2020-10-01
期刊：
影响因子：
3.1
通讯作者：
Li, Lvjie
中科院分区：
文献类型：
--
作者：
Sun, Qian;Li, Qiaojun;Li, Lvjie
相敏光时域反射计分布式光纤振动检测系统的特征提取方法需要先验知识。特征评价方法的缺乏导致模式识别的准确率不高。传统的模式识别方法不能广泛应用。本文提出了一种基于深度学习的振动信号分类识别方法。首先，对振动信号进行时域和空域重构，将其视为脉冲扫描图像；其次，采用移动平均法去除噪声，寻找信号包络面作为图像样本；最后，将图像样本输入到训练好的卷积神经网络（CNN）中，得到识别结果。实验表明，本文提出的基于深度学习的相敏光学时域反射仪脉冲扫描成像模式识别方法在保证识别效率的同时提高了识别精度。该算法易于实现和应用，满足实时在线监测的要求。
Feature extraction method of a phase-sensitive optical time-domain reflectometer distributed optical fiber vibration detection system requires a priori knowledge. A lack of feature evaluation methods leads to a low pattern recognition accuracy. Traditional pattern recognition methods cannot be widely applied. This paper presents the implementation of a deep learning-based method to identify vibration signal categories. First, the vibration signal was reconstructed in the time and space domain, which is regarded as a pulse scanning image. Secondly, moving average was used to reduce noise, and seeking the signal envelope surface as an image sample. Finally, the image sample was inputted into the trained convolutional neural network (CNN) to obtain recognition results. Experiments showed that the phase-sensitive optical time-domain reflectometer pulse scanning imaging pattern recognition method based on deep learning proposed in this paper improved recognition accuracy while ensuring recognition efficiency. The algorithm is easy to implement and apply and satisfies the requirements of real-time online monitoring.