Automated novelty detection using a modified Kohonen self organizing map

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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

Original languageEnglish
Pages (from-to)313-322
Number of pages10
JournalIMechE Transactions Series, 6th National Heat Transfer Conference
Volume2004
Issue number2
Publication statusPublished - 2004

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Automated novelty detection using a modified Kohonen self organizing map'. Together they form a unique fingerprint.

  • Cite this