Developing and Implementing an AI-Based Leak Detection System in a Long-Distance Gas Pipeline

Q3 Engineering
Te-Kwei Wang, Yu-Hsun Lin, Jian-Yuan Shen
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引用次数: 0

Abstract

This research proposes an artificial intelligence (AI) detection model using convolutional neural networks (CNN) to automatically detect gas leaks in a long-distance pipeline. The change of gap pressure is collected when leakage occurs in the pipeline, and thereby the feature of gas leakage is extracted for building the CNN model. The gas leak patterns in the long-distance pipeline are analyzed. A pipeline detection model based on AI technology for automatically monitoring the leaks is proposed by extracting the feature of gas leakage. This model is tested by collecting gas pressure data from an existing natural gas pipeline system starting from Mailiao to Taoyuan in Taiwan. The testing result shows that the reduced model of leak detection can be used to detect the leaks from the upstream and downstream pipelines, and the AI-based pipeline leak detection system can obtain a satisfactory result.
基于人工智能的天然气长输管道泄漏检测系统的开发与实现
本研究提出了一种利用卷积神经网络(CNN)自动检测长输管道气体泄漏的人工智能(AI)检测模型。收集管道发生泄漏时间隙压力的变化,从而提取气体泄漏特征,构建CNN模型。分析了长输管道气体泄漏规律。通过提取气体泄漏特征,提出了一种基于人工智能技术的管道泄漏自动监测模型。通过收集台湾麦寮至桃园现有天然气管道系统的气体压力数据,对该模型进行了验证。测试结果表明,简化的泄漏检测模型可以用于检测上游和下游管道的泄漏,基于人工智能的管道泄漏检测系统可以获得满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Technology Innovation
Advances in Technology Innovation Energy-Energy Engineering and Power Technology
CiteScore
1.90
自引率
0.00%
发文量
18
审稿时长
12 weeks
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