Local discriminant bases in machine fault diagnosis using vibration signals Academic Article

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. 2005 - IOS Press and the author(s). All rights reserved.

authors

• Tafreshi, Reza

published proceedings

• INTEGRATED COMPUTER-AIDED ENGINEERING

author list (cited authors)

• Tafreshi, R., Sassani, F., Ahmadi, H., & Dumont, G.

citation count

• 20

complete list of authors

• Tafreshi, R||Sassani, F||Ahmadi, H||Dumont, G

publication date

• April 2005

publisher

• IOS Press  Publisher

published in

• Integrated Computer Aided Engineering  Journal

Digital Object Identifier (DOI)

• 10.3233/ica-2005-12202

start page

• 147

end page

• 158

volume

• 12

issue

• 2

URL

• http://dx.doi.org/10.3233/ica-2005-12202