Leakage detection in water pipe networks using a Bayesian probabilistic framework

被引:144
|
作者
Poulakis, Z [1 ]
Valougeorgis, D [1 ]
Papadimitriou, C [1 ]
机构
[1] Univ Thessaly, Dept Mech & Ind Engn, Volos 38334, Greece
关键词
system identification; Bayesian method; leakage detection; water pipe networks;
D O I
10.1016/S0266-8920(03)00045-6
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
A Bayesian system identification methodology is proposed for leakage detection in water pipe networks. The methodology properly handles the unavoidable uncertainties in measurement and modeling errors. Based on information from flow test data, it provides estimates of the most probable leakage events (magnitude and location of leakage) and the uncertainties in such estimates. The effectiveness of the proposed framework is illustrated by applying the leakage detection approach to a specific water pipe network. Several important issues are addressed, including the role of modeling error, measurement noise, leakage severity and sensor configuration (location and type of sensors) on the reliability of the leakage detection methodology. The present algorithm may be incorporated into an integrated maintenance network strategy plan based on computer-aided decision-making tools. (C) 2003 Elsevier Ltd. All rights reserved.
引用
收藏
页码:315 / 327
页数:13
相关论文
共 50 条
  • [1] Leakage Detection in Water Pipe Networks Using Electromechanical Impedance (EMI) Based Technique
    Pita, J. L.
    Turra, A. E.
    Filho, J. V.
    [J]. STRUCTURAL HEALTH MONITORING 2013, VOLS 1 AND 2, 2013, : 1524 - +
  • [2] A Bayesian Probabilistic Framework for Rain Detection
    Yao, Chen
    Wang, Ci
    Hong, Lijuan
    Cheng, Yunfei
    [J]. ENTROPY, 2014, 16 (06) : 3302 - 3314
  • [3] Water Leakage Detection Using Neural Networks
    Sabu, Shreya
    Mahinthakumar, Gnanamanikam
    Ranjithan, Ranji
    Levis, James
    Brill, Downey
    [J]. WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS 2021: PLANNING A RESILIENT FUTURE ALONG AMERICA'S FRESHWATERS, 2021, : 1033 - 1040
  • [4] TDR-based Pipe Leakage Detection and Location using Bayesian Inference
    Kim, Taejin
    Woo, Sihyeong
    Youn, Byeng D.
    Huh, Young Cheol
    [J]. 2015 IEEE CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (PHM), 2015,
  • [5] Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks
    Kammouh, Omar
    Gardoni, Paolo
    Cimellaro, Gian Paolo
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2020, 198
  • [6] Leakage fault detection of pneumatic pipe system using neural networks
    Zhang, S
    Asakura, T
    Hayashi, S
    [J]. SICE 2003 ANNUAL CONFERENCE, VOLS 1-3, 2003, : 2482 - 2487
  • [7] Leakage Diagnosis Framework for Water Distribution Networks using ABC
    Rathore, Saruch Satishkumar
    Kallesoe, Carsten Skovmose
    Wisniewski, Rafal
    [J]. IFAC PAPERSONLINE, 2023, 56 (02): : 8067 - 8072
  • [8] Leakage detection in water networks
    Holnicki-Szulc, J
    Kolakowski, P
    Nasher, N
    [J]. JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, 2005, 16 (03) : 207 - 219
  • [9] Gas leakage fault detection of pneumatic pipe system using neural networks
    Zhang, S
    Asakura, T
    Hayashi, S
    [J]. JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS MACHINE ELEMENTS AND MANUFACTURING, 2004, 47 (02) : 568 - 573
  • [10] A Probabilistic Framework to Evaluate Seismic Resilience of Hospital Buildings Using Bayesian Networks
    Liu, Jin
    Zhai, Changhai
    Yu, Peng
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 226