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ZENODO
Report . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Report . 2020
License: CC BY
Data sources: Datacite
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DECOMPOSITION-BASED DATA ANALYSIS WITH HYDRAULIC MODEL CALIBRATION FOR LEAKAGE DETECTION AND ISOLATION

Authors: Wu, Zheng Yi; He, Yekun;

DECOMPOSITION-BASED DATA ANALYSIS WITH HYDRAULIC MODEL CALIBRATION FOR LEAKAGE DETECTION AND ISOLATION

Abstract

This paper proposes an integrated approach for leak detection and localization. The leak detection is undertaken by data analysis in multiple steps including time series data decomposition to ensure stationarity, leakage event detection by statistical process control methods using the correlation analysis of multiple sensor events of monitored flows and pressures. A detected leak event is localized via hydraulic model calibration with the snapshots of flow and pressure data collected during the leakage event. The approach is applied to L-Town with two-year monitoring data and the hydraulic model. The results show that the proposed method is effective at detecting and localizing both incipient leaks and pipe bursts.

Keywords

Data decomposition, statistical process control, leakage detection, model calibration, leakage localization

Cleveland R. B., Cleveland W. S., Mcrae J. E. & Terpenning I. (1990). “STL: A Seasonal-Trend Decomposition Procedure Based on Loess,” Journal of Official Statistics, Vol. 6, No. 1., pp. 3-73.

Mounce S. R. & Boxall J. (2011). “Online Monitoring and Detection” in Water Loss Reduction, Ed. Wu Z. Y., ISBN: 978-1-934493-08-3. Bentley Institute Press, Exton, PA, USA.

Vrachimis, S.G., Eliades D. G., Taormina R., Ostfeld A., Kapelan Z., Liu S., Kyriakou M., Pavlou P., Qiu M. and Polycarpou M.M. (2020). “Battle of the Leakage Detection and Isolation Methods.” The 2nd International CCWI/WDSA Joint Conference, Beijing, China http://www.ccwi-wdsa2020.com/, accessed in Jan. 2020.

Wu, Y. & Liu, S. (2017). “A Review of Data-driven Approaches for Burst Detection in Water Distribution Systems.” Urban Water Journal, DOI:10.1080/1573062X.2017.1279191.

Wu, Z. Y, Sage, P. & Turtle, D. (2010) “Pressure Dependent Leakage Detection Approach and its Application to District Water Systems.” ASCE J. Water Resour. Plann. Manage., Vol. 136, No. 1, pp. 116-128.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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