Identification Of Fault Size Of Rolling Element Bearing From Vibration Signature Analysis For Prognostics

Abstract

Prognostics deals with the estimation of the remaining useful life (RUL) of machine, which is predicated based on their present condition and their future operating condition. Rolling element bearing failure is one of the commonly explained reason for machine breakdown. At the point when machine is in running condition and fault occur in bearing, it is extremely difficult to identify fault size in bearing. Thus, for safety of machine it is necessary to change bearing before fault size increases to a significant level. Further, the replacement of bearing might be extremely costly and on other hand chances can't be taken with safety aspects. Therefor it is necessary to find the RUL of bearing by identifying the spall size of fault from vibration signal. The estimation of spall size from vibration signal is as such difficult. Fault size estimation can be done by decomposition of vibration signal by using discrete wavelet transform and also introducing the autoregressive method, minimum entropy deconvolution and hilbert transform. The decomposed signal is divided into peak corresponding to the ball enter into the fault and exit from the fault. Experiment conducted for various size of faults present on the 25 mm inner diameter polyamide cage deep groove ball bearing. The minimum size of 0.3 mm is detected in the present work for outer race defect with only radial load. Further, experiments are conducted for 1mm defect present at inner race and outer race subjected to both loading condition (radial loading condition as well as combine loading condition). The estimated defect size using the proposed technique is found in close agreement with the actual defect size.