DocumentCode :
743578
Title :
Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition
Author :
Mien Van ; Hee-Jun Kang ; Kyoo-Sik Shin
Author_Institution :
Grad. Sch. of Electr. Eng., Univ. of Ulsan, Ulsan, South Korea
Volume :
8
Issue :
6
fYear :
2014
Firstpage :
571
Lastpage :
578
Abstract :
The presence of faults in the bearings of rotating machinery is usually observed with impulses in the vibration signals. However, the vibration signals are generally non-stationary and usually contaminated by noise because of the compounded background noise present in the measuring device and the effect of interference from other machine elements. Therefore in order to enhance monitoring condition, the vibration signal needs to be properly de-noised before analysis. In this study, a novel fault diagnosis method for rolling element bearings is proposed based on a hybrid technique of non-local means (NLM) de-noising and empirical mode decomposition (EMD). An NLM which removes the noise with minimal signal distortion is first employed to eliminate or at least reduce the background noise present in the measuring device. This de-noised signal is then decomposed into a finite number of stationary intrinsic mode functions (IMF) to extract the impulsive fault features from the effect of interferences from other machine elements. Finally, envelope analyses are performed for IMFs to allow for easier detection of such characteristic fault frequencies. The results of simulated and real bearing vibration signal analyses show that the hybrid feature extraction technique of NLM de-noising, EMD and envelope analyses successfully extract impulsive features from noise signals.
Keywords :
fault diagnosis; rolling bearings; signal denoising; background noise; de-noising; empirical mode decomposition; envelope analyses; fault diagnosis; feature extraction; impulsive fault features; minimal signal distortion; nonlocal means; rolling element bearing; stationary intrinsic mode functions;
fLanguage :
English
Journal_Title :
Science, Measurement & Technology, IET
Publisher :
iet
ISSN :
1751-8822
Type :
jour
DOI :
10.1049/iet-smt.2014.0023
Filename :
6985780
Link To Document :
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