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

Fingerprint

Vibration Signal
Fault Diagnosis
Discriminant
Failure analysis
Wavelet Packet
Orthogonal Basis
Wavelet Coefficients
Ignition
Engine cylinders
Internal combustion engines
Feature Extraction
Normalization
Feature extraction
Wavelets
Fault
Engine
Classifiers
Classifier
Neural Networks
Neural networks

ASJC Scopus subject areas

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

Cite this

Local discriminant bases in machine fault diagnosis using vibration signals. / Tafreshi, Reza; Sassani, F.; Ahmadi, H.; Dumont, G.

In: Integrated Computer-Aided Engineering, Vol. 12, No. 2, 2005, p. 147-158.

Research output: Contribution to journalArticle

Tafreshi, Reza ; Sassani, F. ; Ahmadi, H. ; Dumont, G. / Local discriminant bases in machine fault diagnosis using vibration signals. In: Integrated Computer-Aided Engineering. 2005 ; Vol. 12, No. 2. pp. 147-158.
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