Acoustic Signal Classification by Support Vector Machine for Pipe Crack Early Warning in Smart Water Networks
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 7
Abstract
Uncontrolled pipe breaks are a challenge for water utilities all over the world. This paper describes a technique that enables pipe cracks to be identified at an early stage before they become uncontrolled breaks by utilizing a permanent acoustic monitoring system as part of a smart water network. Multiple acoustic features are selected and extracted from recorded wave files that are associated with proactive repair and uncontrolled pipe break events. The extracted acoustic features and the associated wave file labels (as either crack/leak noise or no crack/leak noise) are used to train a support vector machine model. The trained model has been operationalized in the South Australia Water Corporation’s smart water network analytics platform to process incoming new acoustic wave files in a near-real-time manner. If the acoustic wave file is classified as a pipe crack/leak, an alarm is sent to an investigation crew such that leak localization can be conducted and repairs started. The successful detection of multiple pipe cracks/leaks by the developed model after its implementation proves that it is an effective tool to enable proactive management of pipe breaks in water distribution systems.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. In particular, the sound files for the two validation crack events on King William St. can be provided on request to the corresponding author.
Acknowledgments
The research presented in this paper was supported by the Australian Research Council through the Linkage project (LP180100569). The authors thank SA Water staff Ms. Nicole Arbon, Mr. Luke Dix, Mr. Norman Goh, and Mr. David Carter for their efforts in system monitoring. The authors thank All Water staff Mr. Goran Pazeski-Nikoloski, Mr. Adrian Cavallaro, and Mr. Matthew Maresca for their support in the field investigation.
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Information & Authors
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Published In
Journal of Water Resources Planning and Management
Volume 148 • Issue 7 • July 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 15, 2021
Accepted: Mar 4, 2022
Published online: May 2, 2022
Published in print: Jul 1, 2022
Discussion open until: Oct 2, 2022
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