Bearing damage classification using instantaneous energy density

Abstract

A methodology is proposed for classification of type of defect in a bearing using vibration data. Hilbert-Huang Transform (HHT) is used to obtain Instantaneous energy density (IE) values corresponding to interaction of rolling elements and the defect. IE values are treated as a time series and autocorrelation coefficients (ACs) with varying lags are calculated. Based on the ACs, a Defect Occurrence Index (DOI) is proposed to rank the possibility of a type of bearing damage. Based on the DOI, an adaptive filtering process is used to filter the maximum IE corresponding to the peaks generated during the defect interaction. The main feature of the present approach involves capturing the phenomenon of amplitude modulation (using the approach of autocorrelation) for an inner race defect and uses its absence in the case of outer race defect to distinguish between the inner and outer race defects. Statistical techniques (Chi-square test and Coefficient of variation (CV)) on the filtered IE are used for damage type identification. A new parameter, Defect Severity Value (DSV), is proposed for assessment of the severity of the bearing defect. The proposed methodology is validated on simulated outer/inner race defect, and on two different vibration datasets obtained from seeded defect experiments. The proposed methodology helps uniquely identify the bearing damage.