Robust water leakage detection approach using the sound signals andpattern recognition

摘要

Water supply systems are essential for public health, ease of living, and industrial activity; basic to any modem city.But water leakage is a serious problem as it leads to deficient water supplies, roads caving in, leakage in buildings, andsecondary disasters. Today, the most common leakage detection method is based on human expertise. An expert,using a microphone and headset, listens to the sound of water flowing in pipes and relies on their experience to determineif and where a leak exists.The purpose of this study is to propose an easy and stable automatic leak detection method using acoustics. In thepresent study, 10 leakage sounds, and 10 pseudo-sounds were used to train a Support Vector Machine (SVM) which wasthen tested using 69 sounds. Three features were used in the SVM: average Itakura Distance, maximum ItakuraDistance and the largest eigenvalue as derived from Principal Component Analysis. This paper focuses on the ItakuraDistance, which is a measure of the difference between AR models fitted to two data sets, and is found using theidentified AR model parameters. In this study, 10 leakage sounds are used as a standard reference set of data. Theaverage Itakura Distance is the average difference between a test datum and the 10 reference data. The maximumItakura Distance is the maximum difference between a test datum and the 10 reference data. Using these measures andthe PCA eigenvalues as features for our SVM, classification accuracy of 97.1% was obtained.