Real Field Deployment of a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection: Architectural Issues and Blind Field Test Results

管道(软件) 软件部署 鉴定(生物学) 实时计算 计算机科学 假警报 模式(计算机接口) 领域(数学) 警报 恒虚警率 工程类 嵌入式系统 人工智能 人机交互 电气工程 操作系统 生物 程序设计语言 纯数学 植物 数学
作者
Javier Tejedor,Javier Macías-Guarasa,Hugo F. Martins,Juan Pastor-Graells,Sonia Martín‐López,Pedro Guillén,Guy De Pauw,Filip De Smet,Willy Postvoll,Carl Henrik Ahlen,Miguel Gonzalez-Herraez
出处
期刊:Journal of Lightwave Technology [Institute of Electrical and Electronics Engineers]
卷期号:36 (4): 1052-1062 被引量:78
标识
DOI:10.1109/jlt.2017.2780126
摘要

This paper presents an online augmented surveillance system that aims at real-time monitoring of activities along a pipeline. The system is deployed in a fully realistic scenario and exposed to real activities carried out in unknown places at unknown times within a given test time interval (the so-called blind field tests). We describe the system architecture that includes specific modules to deal with the fact that continuous online monitoring needs to be carried out, while addressing the need of limiting the false alarms at reasonable rates. To the best of our knowledge, this is the first published work in which a pipeline integrity threat detection system is deployed in a realistic scenario (using a fiber optic along an active gas pipeline) and is thoroughly and objectively evaluated under realistic blind conditions. The system integrates two operation modes: the machine+activity identification mode identifies the machine that carries out a certain activity along the pipeline, and the threat detection mode directly identifies if the activity along the pipeline is a threat or not. The blind field tests are carried out in two different pipeline sections: the first section corresponds to the case in which the sensor is close to the sensed area, while the second one places the sensed area about 35 km far from the sensor. Results of the machine+activity identification mode showed an average machine+activity classification rate of 46.6%. For the threat detection mode, eight out of ten threats were correctly detected, with only one false alarm appearing in a 55.5-h sensed period.
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