Smart WDS management: Pipe burst detection using real-Time monitoring data

Anastassia Paula Andrade Borges, Donghwi Jung, Joong Hoon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, advanced and smart techniques are being implemented for improving water distribution system (WDS) management and control. Those methods are mostly based on field data measured in real-Time throughout the system of bigdata characteristics especially with respect to its volume and velocity. An interesting research issue is to investigate how to extract useful information from big data for efficient WDS management and control (e.g., pipe burst and leakage detection). This study applies the Western Electric Company (WEC) method, a statistical process control method, for pipe burst detection which plots field data measured in real-Time around control limits obtained from historical normal field measurements. We investigate the impact of meter location and the number of meters on pipe burst detectability (i.e., detection probability and false alarm rate). Control and out-of-control pipe flow data are synthetically generated by using a hydraulic model of the Austin network and simulating pipe bursts under stochastic demand conditions.

Original languageEnglish
Title of host publication2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538604342
DOIs
Publication statusPublished - 2018 Jun 26
Event2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - San Francisco, United States
Duration: 2017 Apr 42017 Apr 8

Publication series

Name2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings

Conference

Conference2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017
CountryUnited States
CitySan Francisco
Period17/4/417/4/8

Keywords

  • burst detection
  • water distribution systems
  • WEC rules

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Safety, Risk, Reliability and Quality
  • Urban Studies

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  • Cite this

    Borges, A. P. A., Jung, D., & Kim, J. H. (2018). Smart WDS management: Pipe burst detection using real-Time monitoring data. In 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings (pp. 1-4). (2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UIC-ATC.2017.8397573