LAUSR

Abstract A defective rolling element bearing produces a specific vibration pattern that can be used as a diagnostic tool in predictive maintenance. Defect growth from an incipient fault to a fully developed fault alters the vibration pattern significantly. Changes in the statistical features of the acceleration as the defect grows can provide useful information in degradation assessment. The purpose of this paper is to study the trend of the important statistical features in terms of defect size. A physical model with the ability of modeling naturally developed defects has been used for the simulation of the generated vibrations in the rolling element bearings. Defects on the outer race, inner race and rolling element have been considered separately. The vibration signals have been obtained numerically from the model and their statistical features have been calculated for different defect sizes. The behavior of the statistical features including rms, peak, crest factor, kurtosis and level crossing rate have been studied thoroughly and simple relations between the defect size and vibration features have been proposed. Level crossing rate is introduced as a useful feature in the condition monitoring of rolling element bearings. Experimental data of a bearing with a natural defect have been measured using an accelerometer in a test and used to validate the results.