Water Network-Failure Data Assessment
The water-supply system is one of the basic and most important critical infrastructures. Water supply service disruption (water quality or quantity) may have serious consequences in modern societies. Water supply service is subject to various failure modes. Failure modes are specified by their degra...
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Published in | Energies (Basel) Vol. 13; no. 11; p. 2990 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.06.2020
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Subjects | |
Online Access | Get full text |
ISSN | 1996-1073 1996-1073 |
DOI | 10.3390/en13112990 |
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Abstract | The water-supply system is one of the basic and most important critical infrastructures. Water supply service disruption (water quality or quantity) may have serious consequences in modern societies. Water supply service is subject to various failure modes. Failure modes are specified by their degradation mechanisms, criticality, occurrence frequency and intensity. These failure modes have a random nature that impacts on the network disruption indicators, such as disruption frequency, network downtime, network repair time and network back-to-service time, i.e., the network resilience. This paper focuses on the water leakage failure mode. The water leakage failure mode assessment considers the unavoidable annual real water losses and the infrastructure leakage index recommended by the International Water Association’s Water Loss Task Force specialist group. Probabilistic statistical modelling was implemented to assess the seasonal index, the failure rates and the expectation value of the “mean time between failures.” The assessment is based on real operational data of the network. Specific attention is paid to the sensitivity of failures to seasonal variations. The presented methodology of the analysis of the water leakage failure mode is extendable to other failure modes and can help in developing new strategies in the management of the water-supply system in normal operation and crisis situations. |
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AbstractList | The water-supply system is one of the basic and most important critical infrastructures. Water supply service disruption (water quality or quantity) may have serious consequences in modern societies. Water supply service is subject to various failure modes. Failure modes are specified by their degradation mechanisms, criticality, occurrence frequency and intensity. These failure modes have a random nature that impacts on the network disruption indicators, such as disruption frequency, network downtime, network repair time and network back-to-service time, i.e., the network resilience. This paper focuses on the water leakage failure mode. The water leakage failure mode assessment considers the unavoidable annual real water losses and the infrastructure leakage index recommended by the International Water Association’s Water Loss Task Force specialist group. Probabilistic statistical modelling was implemented to assess the seasonal index, the failure rates and the expectation value of the “mean time between failures.” The assessment is based on real operational data of the network. Specific attention is paid to the sensitivity of failures to seasonal variations. The presented methodology of the analysis of the water leakage failure mode is extendable to other failure modes and can help in developing new strategies in the management of the water-supply system in normal operation and crisis situations. |
Author | Pietrucha-Urbanik, Katarzyna Eid, Mohamed Tchórzewska-Cieślak, Barbara |
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Cites_doi | 10.3390/resources9010005 10.3311/pp.ci.2011-2.08 10.3390/ijerph16050767 10.1016/j.ress.2019.106754 10.3390/w9090704 10.3390/environments4030044 10.3390/w12010054 10.1186/s40537-019-0235-y 10.1007/s11269-016-1266-1 10.1016/j.measurement.2019.107026 10.3390/en12173228 10.1016/j.watres.2019.114926 10.20944/preprints201712.0120.v2 10.3390/w11061220 10.1002/cjce.23674 10.3390/su11113189 10.1016/j.measurement.2020.107962 10.1061/(ASCE)WR.1943-5452.0001025 10.1002/cjce.23319 |
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References | Ciaponi (ref_18) 2016; 30 Barton (ref_5) 2019; 164 Geng (ref_24) 2019; 97 ref_14 Roccetti (ref_6) 2019; 6 Onieva (ref_13) 2020; 196 ref_11 ref_10 ref_16 Kowalski (ref_12) 2015; 63 Thornton (ref_19) 2003; 6 Zimoch (ref_17) 2012; 34 Bogardi (ref_3) 2011; 55 ref_22 (ref_15) 2017; 61 ref_20 (ref_9) 2011; 13 ref_1 ref_2 ref_29 ref_28 ref_27 ref_26 ref_8 Geng (ref_21) 2019; 145 ref_4 Hasilova (ref_7) 2020; 149 Geng (ref_23) 2020; 98 Cai (ref_25) 2020; 163 |
References_xml | – ident: ref_10 doi: 10.3390/resources9010005 – ident: ref_28 – volume: 34 start-page: 57 year: 2012 ident: ref_17 article-title: Pressure control as part of risk management for a water-pipe network in service publication-title: Ochr. Srodowiska – volume: 55 start-page: 161 year: 2011 ident: ref_3 article-title: Spatial probabilistic model of pipeline failures publication-title: Period Polytech. Civ. Eng. doi: 10.3311/pp.ci.2011-2.08 – ident: ref_26 – ident: ref_11 doi: 10.3390/ijerph16050767 – volume: 196 start-page: 106754 year: 2020 ident: ref_13 article-title: Prediction of pipe failures in water supply networks using logistic regression and support vector classification publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2019.106754 – ident: ref_1 doi: 10.3390/w9090704 – ident: ref_16 doi: 10.3390/environments4030044 – ident: ref_22 doi: 10.3390/w12010054 – volume: 6 start-page: 70 year: 2019 ident: ref_6 article-title: Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures publication-title: J. Big Data doi: 10.1186/s40537-019-0235-y – volume: 6 start-page: 43 year: 2003 ident: ref_19 article-title: Managing leakage by managing pressure: A practical approach publication-title: Water – volume: 30 start-page: 2021 year: 2016 ident: ref_18 article-title: Modularity-Based Procedure for Partitioning Water Distribution Systems into Independent Districts publication-title: Water Resour. Manag. doi: 10.1007/s11269-016-1266-1 – volume: 149 start-page: 107026 year: 2020 ident: ref_7 article-title: Reliability modelling and analysis of water distribution network based on backpropagation recursive processes with real field data publication-title: Measurement doi: 10.1016/j.measurement.2019.107026 – ident: ref_14 doi: 10.3390/en12173228 – ident: ref_8 – volume: 164 start-page: 114926 year: 2019 ident: ref_5 article-title: Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks publication-title: Water Res. doi: 10.1016/j.watres.2019.114926 – ident: ref_29 – ident: ref_27 – volume: 63 start-page: 155 year: 2015 ident: ref_12 article-title: Monitoring of water distribution system effectiveness using fractal geometry publication-title: Bull. Pol. Acad. Sci. Tech. Sci. – ident: ref_4 doi: 10.20944/preprints201712.0120.v2 – ident: ref_20 doi: 10.3390/w11061220 – volume: 98 start-page: 1307 year: 2020 ident: ref_23 article-title: An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes publication-title: Can. J. Chem. Eng. doi: 10.1002/cjce.23674 – volume: 61 start-page: 1 year: 2017 ident: ref_15 article-title: Comparison of two types of artificial neural networks for predicting failure frequency of water conduits publication-title: Period. Polytech. Civ. Eng. – ident: ref_2 doi: 10.3390/su11113189 – volume: 13 start-page: 693 year: 2011 ident: ref_9 article-title: Application of PHA Method for Assessing Risk of Failure on the Example of Sewage System in the City of Krakow publication-title: Rocz. Ochr. Srodowiska – volume: 163 start-page: 107962 year: 2020 ident: ref_25 article-title: A pointer meter recognition method based on virtual sample generation technology publication-title: Measurement doi: 10.1016/j.measurement.2020.107962 – volume: 145 start-page: 04018094 year: 2019 ident: ref_21 article-title: A Novel Leakage-Detection Method Based on Sensitivity Matrix of Pipe Flow: Case Study of Water Distribution Systems publication-title: J. Water Resour. Plan. Manag. doi: 10.1061/(ASCE)WR.1943-5452.0001025 – volume: 97 start-page: 1129 year: 2019 ident: ref_24 article-title: A fault detection method based on horizontal visibility graph-integrated complex networks: Application to complex chemical processes publication-title: Can. J. Chem. Eng. doi: 10.1002/cjce.23319 |
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SubjectTerms | Aging Consumption Drinking water Failure leakage failure data Polyethylene Polyvinyl chloride probabilistic Regression analysis Statistical analysis statistics Steel pipes Stochastic models water network Water shortages Water supply |
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