Automatic leakage detection for water supply systems using Principal Component Analysis

Maho Tajima, Akira Mita

Research output: Contribution to journalArticle

Abstract

The purpose of this study is to propose an easy and stable automatic leakage detection method using acoustics, and obtain higher accuracy than our previous study. In this study, a Support Vector Machine (SVM) was used for pattern recognition. Approximately, 8 leakage sounds, and 8 pseudo-sounds were used to train the SVM which was then tested using the remaining sounds (at present about 90 data samples have been collected). Eigenvalues which were derived from Principal Component Analysis were used in the SVM as feature vector components. The PCA was done on data representing specific range of frequency of the Power Spectral Density functions as obtained from the divided data. Using the proposed method, the classification accuracy of 90.4% was obtained. This high accuracy result showed that possibility of automatic leakage detection.

Original languageEnglish
Pages (from-to)87-94
Number of pages8
JournalMaterials Forum
Volume33
Publication statusPublished - 2008

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Water supply systems
principal components analysis
Principal component analysis
Support vector machines
leakage
Acoustic waves
acoustics
water
Power spectral density
Probability density function
Pattern recognition
Acoustics
pattern recognition
eigenvalues

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanical Engineering
  • Mechanics of Materials

Cite this

Automatic leakage detection for water supply systems using Principal Component Analysis. / Tajima, Maho; Mita, Akira.

In: Materials Forum, Vol. 33, 2008, p. 87-94.

Research output: Contribution to journalArticle

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