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Rolling element bearings are important parts of rotating machineries. Any defect in their elements causes vibration and damage. Many vibration signal analysis techniques are available for detection and diagnosis of defects in machineries. In time domain vibration analysis techniques, various statistical parameters such as RMS, crest factor, skewness, kurtosis etc. are used for defect detection in bearings. Bearing related parameters like defect type, defect size, shaft speed, radial load etc. affect the bearing vibrations. In this paper, the effects of change in bearing radial load on various time-domain statistical parameters are analyzed. New combination of indicators like Kurtosis × RMS, Kurtosis × Peak, RMS × Peak and new indicators developed by researchers TALAF and THIKAT are analyzed for change in radial load on bearing. Also the effect of outer race defect in bearing on statistical parameters is analyzed. In this paper, the bearing defect data sets with outer and inner race defects provided by Society for Machinery Failure Prevention Technology are used. The results show that these parameters can be used as condition indicators for early fault detection in bearings.

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