Experimental study on enhancement of defect detection and defect size estimation in deep groove ball bearing

Rolling elements bearings are widely used in rotating equipment and machines to support load and to reduce friction. The presence of micron sized defects on the mating surfaces of the bearing components can lead to failure through passage of time. The large size defects on bearing elements can be detected/identified by time domain and frequency domain analysis of its vibration signals. However, it becomes difficult to detect local bearing defects at their initial stage either due to their smaller size or presence of noise. In the present experimental study, detection of local defects like crack and pits on bearing races have been carried out. Vibrations generated by healthy bearing and bearing having circular or rectangular defect on either race of bearing have been analyzed using MATLAB software. Signal to noise ratio of vibration signal has been enhanced through self- adaptive noise cancellation. Moreover, width of rectangular defects and diameter of circular defects have been estimated through ‘db5’ wavelet. The estimated defect sizes have been compared and validated through measured actual crack width or pit diameter. Accuracy of results proves that wavelet analysis of time domain signal can be used with confidence to estimate the width of fatigue crack and pit of the bearing races.