Objective Functions for Transient-Based Pipeline Leakage Detection in a Noisy Environment: Least Square and Matched-Filter
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VIEW CORRECTIONPublication: Journal of Water Resources Planning and Management
Volume 145, Issue 10
Abstract
This paper addresses leak detection in the presence of measurement noise using the inverse transient method (ITM). The unknown leak parameters are determined by optimizing a merit function, which fits the numerically modeled pressures to measurements. Traditionally, the fitting is accomplished by a least-square (LS) objective function that minimizes the L2 distance between the model and data. However, in practical problems where the environment is noisy, the minimum L2 distance may result in some fictitious leaks. This paper proposes an alternative objective function, known as matched-filter (MF) in the literature, which is expected to produce a more robust localization in a noisy environment because it maximizes the signal-to-noise ratio (SNR). This function is then compared with the conventional LS approach by assessment of leak-detection accuracy. It was proved that the MF estimator has smaller mean square error of leak localization than LS when signals have high noise level (). For a low noise level, the two estimators converge to the same results. The conclusions were supported by numerical and experimental case studies.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This work has been supported by Research Grant Council of the Hong Kong SAR, China (Project No. T21-602/15R). Support from Italian MIUR and University of Perugia is also acknowledged within the program Dipartimenti di Eccellenza 2018–2022. The authors would also like to thank all team members of Smart UWSS.
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Journal of Water Resources Planning and Management
Volume 145 • Issue 10 • October 2019
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©2019 American Society of Civil Engineers.
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Received: Aug 1, 2018
Accepted: Feb 20, 2019
Published online: Jul 24, 2019
Published in print: Oct 1, 2019
Discussion open until: Dec 24, 2019
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