Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform

2011 ◽  
Vol 17 (14) ◽  
pp. 2081-2094 ◽  
Author(s):  
PK Kankar ◽  
Satish C Sharma ◽  
SP Harsha
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Continuous Wavelet Transform ◽  
Rolling Element Bearing ◽  
Continuous Wavelet ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing

Bearing Fault Diagnosis Based on Wavelet Transform and ICA

2011 ◽  
Vol 148-149 ◽  
pp. 672-675 ◽  
Author(s):  
Qiang Wu ◽  
Qing Bo He ◽  
Fan Rang Kong ◽  
Yong Bin Liu ◽  
Peng Li
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rolling Element Bearing ◽  
Continuous Wavelet ◽  
Typical Characteristic ◽  
Low Snr ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Envelope Spectrum

The key to fault diagnosis of rolling element bearing is how to find typical characteristic frequencies from low SNR mixed signals. Jointing Continuous Wavelet Transform (CWT) with Independent Component Analysis (ICA)，this paper proposes a method to select wavelet scales with iso-interval frequency and analyze envelope spectrum of independent signal to diagnose the fault of rolling element bearing. Finally, the effectiveness of this method has been verified by practical signal of rolling element bearing.

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