Assessing the operating condition of a domestic distribution system based on the power spectra of high-frequency water consumption signals

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-02-18 DOI:10.1016/j.watres.2025.123320
Justyna Stańczyk , Klara Ramm , Paweł Licznar
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Abstract

Due to the growing widespread use of high-frequency measurements in the water supply industry, new possibilities for precise analyses of water consumption are emerging. Studies on a global scale of the performance of water supply networks, i.e. entire water supply zones DMA (District Metered Area), are widely recognized. The implementation of precise measurements of the volume of water consumed at the local scale, even for individual buildings, allows for drawing up detailed patterns of water consumption, demonstrating the cyclicality of their consumption and the interpretation of behavioral aspects of potable water use. However, there is a research gap in the diagnosis of water supply networks in the use of the power spectra of high-frequency water consumption signals to learn about the periodicity of water consumption at the scale of a single building and to identify abnormal states as a result of a disturbed pattern of its power spectral density (PSD) scaling. The article presents the results of research on the variation in the PSD scaling of water consumption, indicated by periodograms, as a result of a short-term anomalous situation caused by the failure of the domestic water supply system. It is shown that, by means of spectral analysis, it is possible to observe anomalies of different magnitudes in the signal power spectrum. It was also confirmed that the developed method can be further supported by a continuous water consumption approach. The presented methodology can be applied to the water expert systems for a rapid diagnosis of the operational status of the water networks regarding the possible occurrence of water leakage.

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基于高频耗水信号功率谱的生活配电系统运行状态评估
由于高频测量在供水行业的日益广泛使用,精确分析用水量的新可能性正在出现。对全球范围内供水网络性能的研究,即整个供水区DMA(区域水表面积),得到了广泛认可。在地方范围内,甚至对个别建筑物进行用水量的精确测量,可以绘制出详细的水消耗模式,显示其消耗的周期性和对饮用水使用行为方面的解释。然而,利用高频耗水信号的功率谱来了解单个建筑尺度上的耗水周期,并识别其功率谱密度(PSD)标度扰动模式导致的异常状态,在供水网络诊断方面存在研究空白。本文介绍了由生活供水系统故障引起的短期异常情况下,用周期图表示的用水量PSD标度变化的研究结果。结果表明,通过谱分析,可以观察到信号功率谱中不同幅度的异常。还证实了所开发的方法可以通过连续用水量法得到进一步的支持。所提出的方法可应用于供水专家系统,对可能发生漏水的供水网络运行状态进行快速诊断。
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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