基于非带限图信号采样的配水管网传感器安置方法

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Juan Li, Baoyi Cai
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引用次数: 0

摘要

监测配水管网(WDN)需要仔细考虑传感器的布置,这对获取网络的全面信息至关重要。WDN 具有天然的图形结构,这使得图形采样理论在选择监测节点时具有优势。然而,图抽样理论只适用于限制性带限信号,而 WDN 的压力数据属于限制性非带限信号。针对这一问题,本文提出了一种将非带限信号转换为带限信号的近似转换方法,并附有一个最佳频谱阈值公式。该公式用于在图频域中进行频谱筛选,有效地将非带限信号转换为带限信号,保留主要的频率成分,同时忽略较小的频率成分。通过对带限信号进行采样,我们可以找出完美恢复信号的采样节点。这些采样节点可作为监测节点,对 WDN 进行全面检查,并准确定位泄漏点。通过与现有的两种传感器放置优化方法进行比较,我们证明了我们的方法在恢复信号和定位泄漏方面的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor placement method for water distribution networks based on sampling of non-bandlimited graph signals
Monitoring water distribution networks (WDNs) requires careful consideration of sensor placement, which is essential for obtaining comprehensive information about the network. A natural graphical structure underlies WDN, making graph sampling theory advantageous for selecting monitoring nodes. However, graph sampling theory is only applied only to restrictive band-limited signals, while the pressure data of WDN is a restrictive non-band-limited signal. To address this issue, this paper presents an approximate conversion method for transforming non-band-limited signals into band-limited signals, accompanied by an optimal spectrum threshold formula. This formula is used to perform spectral screening in the graph frequency domain, effectively converting non-band-limited signals into band-limited signals that preserve the major frequency components while ignoring smaller-value frequency components. By sampling band-limited signal, we identify sampling nodes that perfectly recover the signal. These sampling nodes act as monitoring nodes that can perform a comprehensive inspection of the WDN and accurately locate leaks. The accuracy of our method in recovering the signal and locating the leak is demonstrated by comparing it with two existing sensor placement optimization methods.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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