Rate of Change Processing of Acoustic Data from a Permanent Monitoring System for Pipe Crack Early Identification: A Case Study
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 2
Abstract
A permanent “leak-before-break” type acoustic monitoring system has been deployed by the South Australian Water Corporation across the Adelaide City Central Business District (CBD) since early 2017. Several analytic approaches applied to the collected acoustic data and results, in terms of reducing the number of uncontrolled pipe main breaks, have been reported. New statistical approaches to the analysis of the data are being continually developed with a statistical method based on the rate of change of the acoustic data within measured spectrums reported in this paper. The method involves the determination of short and long-term noise power level benchmarks, using noise power levels in an empirically determined number of windows with different frequency bin widths, to determine the rate of change of power levels across the frequency spectrum. The rate of change is quantified via the determination of normalized power level changes over weekly and monthly periods and the calculation of noise ratios relative to earlier weekly and monthly periods. Thresholds are applied to determine whether a weekly or monthly change alert should be raised for the investigation of potential cracks.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (including noise magnitude data and/or sound files). A specific pack of data has been assembled for this purpose for the validation data for loggers 184 and 33.
Acknowledgments
The research presented in this paper has been supported by the South Australian Water Corporation through a collaborative research project with the University of Adelaide (Project Code: 56118947) and the Australian Research Council through a Linkage Project (Project Code: LP180100569). The authors thank the staff from Allwater (the operating partner for the South Australian Water Corporation) for their support during the field investigations.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 148 • Issue 2 • February 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 30, 2021
Accepted: Oct 27, 2021
Published online: Dec 9, 2021
Published in print: Feb 1, 2022
Discussion open until: May 9, 2022
ASCE Technical Topics:
- Acoustics
- Case studies
- Continuum mechanics
- Cracking
- Data analysis
- Detection methods
- Engineering fundamentals
- Engineering mechanics
- Fracture mechanics
- Infrastructure
- Methodology (by type)
- Pipe leakage
- Pipeline management
- Pipeline systems
- Pipelines
- Research methods (by type)
- Solid mechanics
- Water and water resources
- Water leakage and water loss
- Water management
- Water pipelines
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- Chi Zhang, Mark L. Stephens, Martin F. Lambert, Bradley J. Alexander, Jinzhe Gong, Acoustic Signal Classification by Support Vector Machine for Pipe Crack Early Warning in Smart Water Networks, Journal of Water Resources Planning and Management, 10.1061/(ASCE)WR.1943-5452.0001570, 148, 7, (2022).
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