Peng Zhaoqiang, Jian Jianan, Wen Hongqiao, Gribok Andrei, Wang Mohan, Liu Hu, Huang Sheng, Mao Zhi-Hong, Chen Kevin P
Opt Express. 2020 Sep 14;28(19):27277-27292. doi: 10.1364/OE.397509.
This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.
本文提出了一个集成技术框架，利用分布式光纤传感技术和人工智能来保护管道免受恶意入侵和管道退化的影响。基于相敏光时域反射仪（φ-OTDR）的分布式声学传感（DAS）系统用于检测沿由直管和急弯弯头组成的管道结构传播和散射的声波。通过飞秒诱导的人工瑞利散射中心提高了DAS系统的信噪比。由DAS系统采集的数据通过基于神经网络的机器学习算法进行分析。该系统在各种外部冲击事件中的识别准确率超过85%，通过监督学习进行缺陷识别的准确率超过94%，通过无监督学习的准确率为71%
