Local discriminant bases in machine fault diagnosis using vibration signals

  • Authors:

  • R. Tafreshi;F. Sassani;H. Ahmadi;G. Dumont

  • Affiliations:

  • Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. E-mail: {tafreshi, sassani}@mech.ubc.ca;Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. E-mail: {tafreshi, sassani}@mech.ubc.ca;Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. E-mail: {ahmadi, guyd}@ece.ubc.ca and Department of Electrical Engineering, U ...;Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. E-mail: {ahmadi, guyd}@ece.ubc.ca

  • Venue:
  • Integrated Computer-Aided Engineering
  • Year:
  • 2005

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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.