首页> 外文期刊>Journal of loss prevention in the process industries >Experimental study of leakage detection of natural gas pipeline using FBG based strain sensor and least square support vector machine
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Experimental study of leakage detection of natural gas pipeline using FBG based strain sensor and least square support vector machine

机译:基于FBG的应变传感器和最小二乘支持向量机的天然气管道泄漏检测实验研究。

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Leakage is the most common cause of natural gas pipeline accidents. This work was devoted to natural gas pipeline leakage detection, which is based on detecting negative pressure wave signals caused by leakage. The FBG strain sensor, which is based on monitoring the hoop strain of a pipeline to detect negative pressure wave signals, is fabricated and experimentally tested. Compared to conventional pressure sensors, FBG strain sensors were shown to be less influenced by noise, and they have the advantage of being a nondestructive sensing method. This makes them ideal for sensing pressure transients, which could be analyzed to detect natural gas pipeline leakage. Toward this objective, a least square support vector machine (LS-SVM) classifier was developed as an automatic leakage detection technique. This technique proved to be effective at detecting leakage. (C) 2014 Elsevier Ltd. All rights reserved.
机译:泄漏是天然气管道事故的最常见原因。这项工作致力于天然气管道泄漏检测,该检测基于检测由泄漏引起的负压波信号。 FBG应变传感器是基于监视管道的环向应变以检测负压波信号而制造的,并进行了实验测试。与传统的压力传感器相比,FBG应变传感器显示出受噪声影响较小,并且具有作为非破坏性传感方法的优势。这使得它们成为检测压力瞬变的理想选择,可以对其进行分析以检测天然气管道泄漏。为了实现这一目标,开发了最小二乘支持向量机(LS-SVM)分类器作为自动泄漏检测技术。事实证明,该技术可有效检测泄漏。 (C)2014 Elsevier Ltd.保留所有权利。

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