A Combined Diagnosis Method Using Wavelet and Hilbert Transform for Bearing

As an important component of the rotating machine, ball bearings are widely used in industrial production. Its operating state directly concerns the safety production and economic benefits. In this paper, wavelet analysis was used to deal with non-stationary characteristic signals of the bearing. Meanwhile, Hilbert envelope spectrum is quite suitable for feature extraction. A combined diagnosis method using wavelet analysis and Hilbert spectrum was used to extract and analyze three kinds of faults of the bearing. The diagnosis results prove that the proposed method is effective for the bearing diagnosis.