Many researchers have addressed the challenge of optimal pressure sensor placement for different purposes, such as leakage detection, model calibration, state estimation, etc. However, pressure data often need to serve multiple purposes, and a method to optimize sensor locations with versatility for various objectives is still lacking. In this paper, a graph-based optimal sensor placement (GOSP) framework is proposed, which aims to provide a robust and all-purpose approach to identify critical points for pressure monitoring. By analysing the spatial variation frequencies of WDN pressures, the relationship between measurements and the global variation of original pressures is established. On this basis, the D-optimality criterion is adopted to formulate the objective of GOSP, which aims to maximize the information on the spatial distribution of pressures that can be obtained from measurements. The new-proposed objective ensures that the sensor locations are compatible with various application scenarios. The proposed method was applied to a real-life distribution network, and was compared with other optimal sensor placement methods oriented towards burst detection and pipe roughness calibration. Based on comparative studies in different scenarios including unknown pressure estimation, burst detection, and model calibration, the effectiveness and robustness of the proposed method have been proved.

中文翻译：

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一种基于图信号分析的给水管网中压力传感器最佳配置的通用方法


许多研究人员已经解决了针对不同目的的最佳压力传感器放置的挑战，例如泄漏检测、模型校准、状态估计等。然而，压力数据通常需要用于多种用途，并且仍然缺乏一种针对各种目标优化传感器位置的方法。在本文中，提出了一种基于图形的最佳传感器放置 （GOSP） 框架，旨在提供一种稳健且通用的方法来识别压力监测的关键点。通过分析 WDN 压力的空间变化频率，建立了测量与原始压力的全局变化之间的关系。在此基础上，采用 D 最优性标准来制定 GOSP 的目标，其目的是最大限度地利用可从测量中获得的压力空间分布信息。新提出的目标可确保传感器位置与各种应用场景兼容。所提出的方法应用于现实生活中的配电网络，并与其他面向爆裂检测和管道粗糙度校准的最佳传感器放置方法进行了比较。通过在未知压力估计、爆破检测和模型校准等不同场景下的对比研究，证明了所提方法的有效性和鲁棒性。