Robust water leakage detection approach using the sound signals and pattern recognition

Yuriko Terao, Akira Mita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Volume6932
DOIs
Publication statusPublished - 2008
EventSensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008 - San Diego, CA, United States
Duration: 2008 Mar 102008 Mar 13

Other

OtherSensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008
CountryUnited States
CitySan Diego, CA
Period08/3/1008/3/13

Fingerprint

pattern recognition
Pattern recognition
leakage
Acoustic waves
acoustics
Support vector machines
water
Water
eigenvalues
Water supply systems
Leak detection
earphones
public health
Public health
Microphones
Water supply
disasters
Principal component analysis
Disasters
principal components analysis

Keywords

  • Itakura distance
  • Leak detection
  • Support vector machine

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

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.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
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