Feature selection and classification of high-resolution NMR spectra in the complex wavelet transform domain

2008 ◽  
Vol 90 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Seoung Bum Kim ◽  
Zhou Wang ◽  
Soontorn Oraintara ◽  
Chivalai Temiyasathit ◽  
Yodchanan Wongsawat
Keyword(s):  
Feature Selection ◽  
Wavelet Transform ◽  
High Resolution ◽  
Nmr Spectra ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
High Resolution Nmr ◽  
Complex Wavelet

Compound Fault Feature Separation of Rolling Bearing Based on Complex Wavelet and Energy Operator Demodulation

2012 ◽  
Vol 226-228 ◽  
pp. 765-771
Author(s):  
Yang Yang ◽  
Jian Yu Zhang ◽  
Sui Zheng Zhang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difficult Problem ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Transform Coefficients ◽  
Complex Wavelet ◽  
Fault Information

Compound fault feature separation is a difficult problem in diagnosis field of mechanical system. For the rolling bearing with compound fault on outer and inner race, feature separation technology based on complex wavelet transform and energy operator demodulation is introduced. Through continuous wavelet transform, coefficients of mixed fault signal can be achieved in different wavelet transform domain (i.e. real, imaginary, modulus and phase domain). Furthermore, wavelet power spectrum contours and time average wavelet energy spectrum are applied to extract the scales which hold rich fault information, and the wavelet coefficient slice of specific scale is also drawn. For wavelet coefficients in different domain, spectrum analysis and energy operator demodulation can be used successfully to separate mixed fault. The comparison of feature extraction effect between complex wavelet and real wavelet transform shows that complex wavelet transform is obviously better than the latter.

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