Local discriminant bases in machine fault diagnosis using vibration signals

TitleLocal discriminant bases in machine fault diagnosis using vibration signals
Publication TypeJournal Article
Year of Publication2005
AuthorsTafreshi, R., F. Sassani, H. Ahmadi, and G. Dumont
JournalIntegrated Computer-Aided Engineering
Volume12
Pagination147–158
ISSN1069-2509
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.

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