Visible to the public An Efficient Approach to Fault Identification in Urban Water Networks Using Multi-Level SensingConflict Detection Enabled

TitleAn Efficient Approach to Fault Identification in Urban Water Networks Using Multi-Level Sensing
Publication TypeConference Paper
Year of Publication2015
AuthorsAbbas, Waseem, Perelman, Lina Sela, Amin, Saurabh, Koutsoukos, Xenofon
Conference NameProceedings of the 2Nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments
Conference LocationSeoul, South Korea
ISBN Number978-1-4503-3981-0
Keywordsfault localization, minimum test cover, SURE Project, water networks

The objective of this work is to develop an efficient and practical sensor placement method for the failure detection and localization in water networks. We formulate the problem as the minimum test cover problem (MTC) with the objective of selecting the minimum number of sensors required to uniquely identify and localize pipe failure events. First, we summarize a single-level sensing model and discuss an efficient fast greedy approach for solving the MTC problem. Simulation results on benchmark test networks demonstrate the efficacy of the fast greedy algorithm. Second, we develop a multi-level sensing model that captures additional physical features of the disturbance event, such as the time lapsed between the occurrence of disturbance and its detection by the sensor. Our sensor placement approach using MTC extends to the multi-level sensing model and an improved identification performance is obtained via reduced number of sensors (in comparison to single-level sensing model). In particular, we investigate the bi-level sensing model to illustrate the efficacy of employing multi-level sensors for the identification of failure events. Finally, we suggest extensions of our approach for the deployment of heterogeneous sensors in water networks by exploring the trade-off between cost and performance (measured in terms of the identification score of pipe/link failures).

Citation KeyAbbas:2015:EAF:2821650.2821666