Water leak detection based on convolutional neural network using actual leak sounds and the hold-out method

Abstract The main purpose of this study was to investigate whether machine learning can be used to detect leak sounds in the field. A method for detecting water leaks was developed using a convolutional neural network (CNN), after taking recurrence plots and visualising the time series as input data...

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Bibliographic Details
Published inWater science & technology. Water supply Vol. 21; no. 7; pp. 3477 - 3485
Main Authors Nam, Y. W., Arai, Y., Kunizane, T., Koizumi, A.
Format Journal Article
LanguageEnglish
Published London IWA Publishing 01.11.2021
Subjects
Acoustics
actual leaks
Artificial neural networks
Capital expenditures
convolutional neural network
Detection
hold-out method
Investigations
Leak detection
Machine learning
Methods
Neural networks
recurrence plot
Restoration
Sensors
Software
Sound
Steel pipes
Time series
water leak detection
water pipeline
Water pipes
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Summary:Abstract The main purpose of this study was to investigate whether machine learning can be used to detect leak sounds in the field. A method for detecting water leaks was developed using a convolutional neural network (CNN), after taking recurrence plots and visualising the time series as input data. In collaboration with a pipeline restoration company, 20 acoustic datasets of leak sounds were recorded by sensors at 10 leak sites. The detection ability of the constructed CNN model was tested using the hold-out method for the 20 cases: 19 showed more than 70% accuracy, of which 15 showed more than 80%.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2021.109