Statistical and frequency analysis of vibrations signals of roller bearings using empirical mode decomposition

In the present work, defect detection in rolling bearing using empirical mode decomposition of vibration signal data has been done. Higher order statistical parameters viz root mean square, kurtosis, skewness, and crest factor are utilized to diagnose bearing fault. Simulated bearing defects as spall on outer race, on roller, and on inner race are used for the study. For experimental study, four different load and speed combination have been chosen to widen the acceptability of results. The effect of bearing speed on statistical parameters is also studied. Effectiveness of signal decomposition by the empirical mode decomposition method has been established by the results. Kurtosis and crest factor values of decomposed and unprocessed signals have been selected and empirical mode decomposition-based values are shown as effective parameters for defect identification. The crest factor and Kurtosis of outer race defect show greater sensitivity to the load and speed variations, while the skewness of same defect shows its insensitivity to load and speed variations.