Analysis of autogram performance for rolling element bearing diagnosis by using different data sets

Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.