Modified self-organising map for automated novelty detection applied to vibration signal monitoring

M. L. D. Wong, L. B. Jack, A. K. Nandi

Research output: Contribution to journalArticle

86 Citations (Scopus)

Abstract

This paper proposes a novelty detection-based method for machine condition monitoring (MCM) using vibration signals and a new feature extraction method based on higher-order statistics of the power spectral density. This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification. The approach is designed to be highly modular and scale well for a multi-sensor condition monitoring environment. Experiments using real-world vibration data sets with upto eight sensors have shown high accuracy in classification and robustness across different condition monitoring applications.

Original languageEnglish
Pages (from-to)593-610
Number of pages18
JournalMechanical Systems and Signal Processing
Volume20
Issue number3
DOIs
Publication statusPublished - Apr 2006

Keywords

  • Artificial neural network
  • Condition monitoring
  • Features extraction
  • Higher-order statistics
  • Novelty detection
  • Self-organising map
  • Vibration signal processing

ASJC Scopus subject areas

  • Signal Processing
  • Mechanical Engineering

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