Local discriminant bases in machine fault diagnosis using vibration signals

Reza Tafreshi, F. Sassani, H. Ahmadi, G. Dumont

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Wavelets and local discriminant bases (LDB) selection algorithm is applied to vibration signals in a single-cylinder spark ignition engine for feature extraction and fault classification. LDB selects a complete orthogonal basis from a wavelet packet library of bases, which best discriminates the given classes, based on their time-frequency energy maps. An appropriate normalization method in both data and wavelet coefficient domains, and a neural network classifier during the identification phase are used to enhance the classification. By applying LDB to a real-world machine data the accuracy of the algorithm in machine fault diagnosis and classification is shown.

Original languageEnglish
Pages (from-to)147-158
Number of pages12
JournalIntegrated Computer-Aided Engineering
Volume12
Issue number2
Publication statusPublished - 2005
Externally publishedYes

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ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Computational Theory and Mathematics

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