C4.5: programs for machine learning
C4.5: programs for machine learning
Essential wavelets for statistical applications and data analysis
Essential wavelets for statistical applications and data analysis
The lifting scheme: a construction of second generation wavelets
SIAM Journal on Mathematical Analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme
Mathematics and Computers in Simulation
Classification of audio signals using SVM and RBFNN
Expert Systems with Applications: An International Journal
Real time face and mouth recognition using radial basis function neural networks
Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Nonlinear wavelet transforms for image coding via lifting
IEEE Transactions on Image Processing
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Wavelet transform has been widely used for the vibration signal based mechanical equipment fault diagnosis. However, the decomposition results of the discrete wavelet transform do not possess time invariant property, which may result in the loss of useful information and decrease the classification accuracy of fault diagnosis. To overcome this deficiency, a novel fault diagnosis method based on the redundant second generation wavelet packet transform is proposed. Firstly, the redundant second generation wavelet packet transform is constructed on the basis of second generation wavelet transform and redundant lifting scheme. Secondly, the vibration signals are decomposed by redundant second generation wavelet packet transform and then the faulty features are extracted from the resultant wavelet packet coefficients. Finally, the extracted fault features are given as input to classifiers for identification. The proposed method is applied for the fault diagnosis of gearbox and gasoline engine valve trains. Test results indicate that a better classification performance can be obtained by using the proposed fault diagnosis method in comparison with using second generation wavelet packet transform based method.