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

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.

author list (cited authors)

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

citation count

  • 20

publication date

  • April 2005