Detection and Characterization of Bearing Faults from the Frequency Domain Features of Vibration

The characteristics of vibrations is one which is widely used for the non-intrusive inspection and health monitoring of bearings. However, automated methods, intended for predicting the health status of bearings greatly depend on the features extracted from the vibration signal. In this paper, the ability of frequency domain features such as spectral role-off (SR), median frequency (MF), spectral centroid (SC), dominant frequency (DF), and spectral flux (SF) of the bearing vibration data corresponding to healthy, inner race failure (IRF), roller element defect (RED), and outer race failure (ORF) to identify the state of the bearing is analyzed. The SF, DF, and SC are identified directly from the vibration spectra. The MF and SR are computed from the power spectral density estimate using an analytical method. Before computing the spectrum, the vibration signal is preconditioned with offset elimination and normalization. The normalized data is windowed with Hanning window to suppress the ripples induced in the spectrum during the computation of fast Fourier transform. It has been observed that among the features, MF and SC characterize the status of bearing and the type of faults better than other features. MF is useful to distinguish healthy bearing from IRF and IRF from RED. SC is useful to distinguish IRF from RED and IRF from ORF. The SR, MF, SC, DF, and SF corresponding to the vibrations acquired from normal and faulty bearings differ with a "P" value of 2.22045 x 10(-16), approximate to 0, 1.11022 x 10(-16), 0.0008, and 2.35957 x 10(-8), respectively, for a level of significance 0.05. SR, MF, and SC are statistically more significant than DF and SF.