iPipe: Water Pipeline Monitoring and Leakage Detection

Sakshi Singh, Shalini Agrawal, Tina Sahu, Debanjan Das
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Abstract

Pipelines are considered to be a part of the transportation sector. The pipeline industry moves various substances such as crude oil, refined petroleum products, and natural gas within thousands of miles of pipelines. There may be various reasons that can cause the failure of these pipeline systems, resulting in some serious disasters. So, it is necessary to detect the leakage of water and oil pipelines by which the accidents can be avoided and damage is minimal. This paper proposes iPipe, i.e. an intelligent water pipeline monitoring and leakage detection system that is based on an acoustic signal method to detect and locate leaks in pipelines by using a network of acoustic and GPS sensors to continuously monitor the sound in the vicinity of the pipe. The system uses signal processing to identify the frequency and characteristics of leak sound and machine learning techniques to differentiate between characteristic sound and the normal sounds in the environment near the pipe. The sensors provide information about the leak as soon as possible to the designated endpoint i.e, cloud server. All of them have different locations and IDs so that it is possible to know where the data came from. This work reduces the need for human intervention by automatically notifying about the leakage. The proposed system has the ability to detect leaks with an accuracy of 95.6%.
iPipe:供水管道监测和泄漏检测
管道被认为是运输部门的一部分。管道工业在数千英里的管道内运输各种物质,如原油、精炼石油产品和天然气。可能有各种各样的原因导致这些管道系统的故障,从而导致一些严重的灾难。因此,有必要对管道的水、油泄漏进行检测,以避免事故的发生,并将损失降到最低。本文提出了iPipe,即一种基于声信号方法的智能水管监测和泄漏检测系统,通过声学和GPS传感器网络对管道附近的声音进行持续监测,从而检测和定位管道中的泄漏。该系统使用信号处理来识别泄漏声音的频率和特征,并使用机器学习技术来区分管道附近环境中的特征声音和正常声音。传感器会尽快向指定端点(即云服务器)提供有关泄漏的信息。它们都有不同的位置和id,因此可以知道数据来自哪里。这项工作通过自动通知泄漏,减少了人工干预的需要。该系统检测泄漏的准确率为95.6%。
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