An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet. (31st July 2008)

• Record Type: Journal Article
• Authors: Chen, Huaxin; Chen, Xuefeng; Zi, Yanyang; Ding, Feng; Cao, Hongrui; Tan, Jiyong; Jiang, Hongkai; He, Zhengjia
• Is Part Of: International journal of material & product technology. Volume 33:Number 1/2(2008)
• Page Start: 65
• Page End: 78
• Publication Date: 2008-07-31
• Subjects: rolling bearings -- fault diagnosis -- adaptive redundant wavelet -- second-generation wavelet -- ARSGW -- lifting scheme -- Hilbert transform -- vibration signals -- signal de-noising -- signal extraction -- machine tool spindles; Manufacturing processes -- Periodicals; Materials -- Testing -- Periodicals; 620.11
• Languages: English
• ISSNs: 0268-1900

Abstract

De-noising and extraction of weak signals are crucial to fault prognostics, and the wavelet transform has been widely used in signal de-noising. In this paper, a new method, which combines the Adaptive Redundant Second-Generation Wavelet (ARSGW) and the Hilbert transform, is proposed. The ARSGW is applied to reveal the transient components of the signal in time domain clearly. Then the Hilbert transform is used to extract fault features of rolling bearing from the wavelet packets. The analysis results of the vibration signals from the experiment and the machine tool spindle show that the proposed method can detect the faults of the rolling bearing effectively.