基于声学信号的混合机器学习漏水检测系统

IF 1.6 3区 环境科学与生态学 Q3 WATER RESOURCES Urban Water Journal Pub Date : 2023-08-01 DOI:10.1080/1573062X.2023.2239782
M. Saravanabalaji, N. Sivakumaran, S. Ranganthan, V. Athappan
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引用次数: 0

摘要

摘要供水管道泄漏是世界范围内的一个重大问题。泄漏检测和修复可以防止缺水和其他一些问题。因此,研究管道泄漏检测技术具有较高的实用价值。这项研究采用了一种很有前途的技术,利用声发射(AE)信号检测管道泄漏。采用dytran加速度传感器对泄漏信号进行时域采集。时域信号通过采用快速傅立叶变换(FFT)被变换到频域。所产生的频率信号具有许多维度,这些维度可以通过主成分分析(PCA)减少到17。智能泄漏诊断技术应消除时间和人为干预,并提高有效性。机器学习(ML)模型在这一点上发挥了作用。为了检测泄漏,提出了混合ML模型,并与传统的ML模型进行了比较。使用分类度量来识别用于检测漏水的最佳模型。
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Acoustic signal based water leakage detection system using hybrid machine learning model
ABSTRACT Water supply pipeline leakage is a major issue around the world. Leak detection and remediation can prevent water scarcity and some other problems. As a result, investigating pipeline leak-detecting technology has a high practical value. This study employs a promising technique for detecting pipeline leaks using Acoustic Emission (AE) signals. A dytran acceleration sensor was used to collect leakage signals in the time domain. The time-domain signal is transformed into a frequency domain by employing Fast Fourier Transform (FFT). The produced frequency signal has many dimensions which can be reduced to 17 by Principal Component Analysis (PCA). Intelligent leakage diagnosis techniques should eliminate time, and human intervention, and increase effectiveness. Machine learning (ML) models come into play at this point. To detect leakage, the hybrid ML model is proposed and it is compared with the conventional ML models. The best model for detecting water leakage is identified using the classification metrics.
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来源期刊
Urban Water Journal
Urban Water Journal WATER RESOURCES-
CiteScore
4.40
自引率
11.10%
发文量
101
审稿时长
3 months
期刊介绍: Urban Water Journal provides a forum for the research and professional communities dealing with water systems in the urban environment, directly contributing to the furtherance of sustainable development. Particular emphasis is placed on the analysis of interrelationships and interactions between the individual water systems, urban water bodies and the wider environment. The Journal encourages the adoption of an integrated approach, and system''s thinking to solve the numerous problems associated with sustainable urban water management. Urban Water Journal focuses on the water-related infrastructure in the city: namely potable water supply, treatment and distribution; wastewater collection, treatment and management, and environmental return; storm drainage and urban flood management. Specific topics of interest include: network design, optimisation, management, operation and rehabilitation; novel treatment processes for water and wastewater, resource recovery, treatment plant design and optimisation as well as treatment plants as part of the integrated urban water system; demand management and water efficiency, water recycling and source control; stormwater management, urban flood risk quantification and management; monitoring, utilisation and management of urban water bodies including groundwater; water-sensitive planning and design (including analysis of interactions of the urban water cycle with city planning and green infrastructure); resilience of the urban water system, long term scenarios to manage uncertainty, system stress testing; data needs, smart metering and sensors, advanced data analytics for knowledge discovery, quantification and management of uncertainty, smart technologies for urban water systems; decision-support and informatic tools;...
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