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

Y. W. Nam,Y. Arai,A. Koizumi,T. Kunizane

doi:10.2166/ws.2021.109

Abstract

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%.

Highlights

Capital expenditure on supplying drinking water was approximately US$90 billion in 2011
The recurrence plots (RP) of background noise in Figure 4 are close to white noise and their shapes tend not to have regular features
It can be qualitatively determined that the RPs of water leak sounds exhibit shapes with regular features, such as a mesh or honeycomb

Summary

Introduction

Capital expenditure on supplying drinking water was approximately US$90 billion in 2011. The situation is similar in Japan: of the total assets of the water supply system, water pipes account for about 65% of the economic value The ratio of ageing pipes that need to be replaced, which was 6% in 2006, is increasing every year, while the rate of pipeline renewal has been falling steadily (Ministry of Health Labour & Welfare ). This figure is expected to exceed 20% in 10 years and 40% in 20 years

Objectives

Methods

Results

Conclusion