Φ-OTDR系统振动信号的聚类识别方法  

Cluster-based recognition method forΦ-OTDR system's vibration signals

作　　者：刘念超 李勤 赵小艇[1] 梁生 LIU Nianchao;LI Qin;ZHAO Xiaoting;LIANG Sheng(School of Physical Science and Engineering,Beijing Jiaotong University,Beijing 100044,China;Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology,Langfang 065000,China;The Third Research Institute of CETC,Beijing 100015,China)

机构地区：[1]北京交通大学物理科学与工程学院,北京100044 [2]河北省地震灾害仪器与监测技术重点实验室,河北廊坊065000 [3]中国电子科技集团公司第三研究所,北京100015

出　　处：《红外与激光工程》2024年第11期186-200,共15页Infrared and Laser Engineering

基　　金：河北省地震灾害仪器与监测技术重点实验室开放基金(FZ224107)。

摘　　要：分布式光纤声学传感(DAS)利用单模通信光纤即可实现大规模、低成本的传感阵列,针对基于相位敏感光时域反射(Φ-OTDR)技术的DAS系统产生的大量数据中有效信息稀缺的问题,提出了一种基于时域振幅特征提取和无监督聚类的方法,旨在探索无监督学习在DAS入侵事件识别中的应用。该方法通过计算相邻数据点差值、提取关键特征,并使用层次聚类对振动事件进行分类,相较于主流神经网络算法,需更少样本且无需手动标记。实验模拟了风噪声、人工敲击和挖掘三种振动事件,结果表明,该方法的V度量为0.721、剪影系数为0.778、聚类准确率可达88.68%,有效区分了入侵信号与噪声,并基本分辨出人工敲击与挖掘信号,证明聚类识别可应用于Φ-OTDR事件识别。Objective Distributed fiber optic acoustic sensing(DAS),as a novel vibration sensing technology,leverages single-mode communication fibers to create large-scale,cost-effective sensing arrays.Although this sensing technology is more prone to noise interference compared to traditional strain sensors,it offers a broader response bandwidth and greater durability for long-term deployments.Consequently,it has found widespread applications in various fields,including seismic wave detection,pipeline condition monitoring,perimeter vibration sensing,and more.The DAS system produces a significant amount of data during operation;However,only a fraction of this data contains relevant information.The prevailing approach involves employing machine learning for classification or pattern recognition tasks,thereby maximizing the utilization of the data's value.Since many fiber vibration event recognition methods rely on extracting features from time-frequency domain graphs to accomplish the classification task,and such methods tend to complicate the algorithm due to the incorporation of convolutional neural networks(CNN),this study aims to simplify the process by combining basic data preprocessing with clustering algorithms to categorize vibration signals.Methods A new method based on time-domain amplitude feature extraction using clustering algorithm for intrusion event recognition is proposed(Fig.2).This method can be used in phase-sensitive optical time-domain reflectometer(Φ-OTDR)to classify the detected vibration signals.Compared with traditional image machine learning algorithms,the signal recognition method proposed in this study requires fewer samples and does not need tedious manual labeling.In this method,firstly,the difference of neighboring data points is calculated to get the maximum value of the difference sequence,and the maximum and envelope values are used to extract key features(Fig.3).Then,the vibration events are classified using hierarchical clustering algorithms.Finally,the effectiveness of the method is ver

关 键 词：Φ-OTDR 时域特征提取 聚类分析 数据裁剪 

分 类 号：TN247[电子电信—物理电子学]