Pipeline monitoring using acoustic principal component analysis recognition with the Mel scale

Chunfeng Wan, Akira Mita

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In modern cities, many important pipelines are laid underground. In order to prevent these lifeline infrastructures from accidental damage, monitoring systems are becoming indispensable. Third party activities were shown by recent reports to be a major cause of pipeline damage. Potential damage threat to the pipeline can be identified by detecting dangerous construction equipment nearby by studying the surrounding noise. Sound recognition technologies are used to identify them by their sounds, which can easily be captured by small sensors deployed along the pipelines. Pattern classification methods based on principal component analysis (PCA) were used to recognize the sounds from road cutters. In this paper, a Mel residual, i.e.the PCA residual in the Mel scale, is proposed to be the recognition feature. Determining if a captured sound belongs to a road cutter only requires checking how large its Mel residual is. Experiments were conducted and results showed that the proposed Mel-residual-based PCA recognition worked very well. The proposed Mel PCA residual recognition method will be very useful for pipeline monitoring systems to prevent accidental breakage and to ensure the safety of underground lifeline infrastructures.

Original languageEnglish
Article number055004
JournalSmart Materials and Structures
Volume18
Issue number5
DOIs
Publication statusPublished - 2009

Fingerprint

principal components analysis
Principal component analysis
Pipelines
Acoustics
acoustics
Acoustic waves
Monitoring
cutters
damage
roads
Construction equipment
Acoustic noise
Pattern recognition
safety
causes
sensors
Sensors
Experiments

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Civil and Structural Engineering
  • Condensed Matter Physics
  • Mechanics of Materials
  • Materials Science(all)

Cite this

Pipeline monitoring using acoustic principal component analysis recognition with the Mel scale. / Wan, Chunfeng; Mita, Akira.

In: Smart Materials and Structures, Vol. 18, No. 5, 055004, 2009.

Research output: Contribution to journalArticle

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