Robust water leakage detection approach using the sound signals and pattern recognition
Yuriko Terao, Akira Mita
Department of System Design Engineering
Water supply systems are essential for public health, ease of living, and industrial activity; basic to any modern city. But water leakage is a serious problem as it leads to deficient water supplies, roads caving in, leakage in buildings, and secondary 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 determine if and where a leak exists. The purpose of this study is to propose an easy and stable automatic leak detection method using acoustics. In the present study, 10 leakage sounds, and 10 pseudo-sounds were used to train a Support Vector Machine (SVM) which was then tested using 69 sounds. Three features were used in the SVM: average Itakura Distance, maximum Itakura Distance and the largest eigenvalue as derived from Principal Component Analysis. This paper focuses on the Itakura Distance, which is a measure of the difference between AR models fitted to two data sets, and is found using the identified AR model parameters. In this study, 10 leakage sounds are used as a standard reference set of data. The average Itakura Distance is the average difference between a test datum and the 10 reference data. The maximum Itakura Distance is the maximum difference between a test datum and the 10 reference data. Using these measures and the PCA eigenvalues as features for our SVM, classification accuracy of 97.1 % was obtained.
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008 - San Diego, CA, United States
Duration: 2008 Mar 10 → 2008 Mar 13
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008
leakage
Acoustic waves
Water supply systems
principal components analysis
Itakura distance
Terao, Y., & Mita, A. (2008). Robust water leakage detection approach using the sound signals and pattern recognition. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 6932). [69322D] https://doi.org/10.1117/12.775968
Robust water leakage detection approach using the sound signals and pattern recognition. / Terao, Yuriko; Mita, Akira.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6932 2008. 69322D.
Terao, Y & Mita, A 2008, Robust water leakage detection approach using the sound signals and pattern recognition. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 6932, 69322D, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008, San Diego, CA, United States, 08/3/10. https://doi.org/10.1117/12.775968
Terao Y, Mita A. Robust water leakage detection approach using the sound signals and pattern recognition. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6932. 2008. 69322D https://doi.org/10.1117/12.775968
Terao, Yuriko ; Mita, Akira. / Robust water leakage detection approach using the sound signals and pattern recognition. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6932 2008.
@inproceedings{3e1dc7603d8140d09f2a79036355ad18,
title = "Robust water leakage detection approach using the sound signals and pattern recognition",
abstract = "Water supply systems are essential for public health, ease of living, and industrial activity; basic to any modern city. But water leakage is a serious problem as it leads to deficient water supplies, roads caving in, leakage in buildings, and secondary 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 determine if and where a leak exists. The purpose of this study is to propose an easy and stable automatic leak detection method using acoustics. In the present study, 10 leakage sounds, and 10 pseudo-sounds were used to train a Support Vector Machine (SVM) which was then tested using 69 sounds. Three features were used in the SVM: average Itakura Distance, maximum Itakura Distance and the largest eigenvalue as derived from Principal Component Analysis. This paper focuses on the Itakura Distance, which is a measure of the difference between AR models fitted to two data sets, and is found using the identified AR model parameters. In this study, 10 leakage sounds are used as a standard reference set of data. The average Itakura Distance is the average difference between a test datum and the 10 reference data. The maximum Itakura Distance is the maximum difference between a test datum and the 10 reference data. Using these measures and the PCA eigenvalues as features for our SVM, classification accuracy of 97.1 {\%} was obtained.",
keywords = "Itakura distance, Leak detection, Support vector machine",
author = "Yuriko Terao and Akira Mita",
T1 - Robust water leakage detection approach using the sound signals and pattern recognition
AU - Terao, Yuriko
AU - Mita, Akira
N2 - Water supply systems are essential for public health, ease of living, and industrial activity; basic to any modern city. But water leakage is a serious problem as it leads to deficient water supplies, roads caving in, leakage in buildings, and secondary 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 determine if and where a leak exists. The purpose of this study is to propose an easy and stable automatic leak detection method using acoustics. In the present study, 10 leakage sounds, and 10 pseudo-sounds were used to train a Support Vector Machine (SVM) which was then tested using 69 sounds. Three features were used in the SVM: average Itakura Distance, maximum Itakura Distance and the largest eigenvalue as derived from Principal Component Analysis. This paper focuses on the Itakura Distance, which is a measure of the difference between AR models fitted to two data sets, and is found using the identified AR model parameters. In this study, 10 leakage sounds are used as a standard reference set of data. The average Itakura Distance is the average difference between a test datum and the 10 reference data. The maximum Itakura Distance is the maximum difference between a test datum and the 10 reference data. Using these measures and the PCA eigenvalues as features for our SVM, classification accuracy of 97.1 % was obtained.
AB - Water supply systems are essential for public health, ease of living, and industrial activity; basic to any modern city. But water leakage is a serious problem as it leads to deficient water supplies, roads caving in, leakage in buildings, and secondary 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 determine if and where a leak exists. The purpose of this study is to propose an easy and stable automatic leak detection method using acoustics. In the present study, 10 leakage sounds, and 10 pseudo-sounds were used to train a Support Vector Machine (SVM) which was then tested using 69 sounds. Three features were used in the SVM: average Itakura Distance, maximum Itakura Distance and the largest eigenvalue as derived from Principal Component Analysis. This paper focuses on the Itakura Distance, which is a measure of the difference between AR models fitted to two data sets, and is found using the identified AR model parameters. In this study, 10 leakage sounds are used as a standard reference set of data. The average Itakura Distance is the average difference between a test datum and the 10 reference data. The maximum Itakura Distance is the maximum difference between a test datum and the 10 reference data. Using these measures and the PCA eigenvalues as features for our SVM, classification accuracy of 97.1 % was obtained.
KW - Itakura distance
KW - Leak detection
KW - Support vector machine