Statistical Analysis of Vibration Signal Frequency During Inner Race Fault of Rolling Ball Bearings.

This research paper introduces a novel approach using dominant frequency analysis to diagnose inner race faults in rolling ball bearings of three-phase induction motor. The main objective of the proposed scheme is to identify damaged bearings by analyzing their characteristic frequency components within a specific time interval in segmented signal. This work has been carried out on vibration signal data provided by Bearing Center Case Reserve Western University (CWRU), USA. In this study, IR007, IR014 and IR021 bearing defects are analyzed by Frequency Statistical Analysis in MATLAB. Mean and standard deviation of dominant frequencies are computed from the recorded vibration signals after dividing the signal into multiple segments of equal length. It is observed that both frequency mean, and standard deviation have been found to be highly sensitive with variations of motor speed and connected load. Therefore, motor speed is also studied to calculate the statistical parameters. The test results suggest that the proposed scheme provides comprehensive information about fault analysis through vibration data and could potentially aid researchers in fault analysis using the CRWU datasets. Overall, this paper presents a promising approach to diagnose the faults in the inner race of rolling ball bearings using frequency domain analysis and statistical parameters. [ABSTRACT FROM AUTHOR]