Rushit Shaha, Michael H. Azariana, and Michael G. Pecht a
a CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, USA
Abstract:
Autocorrelation is a special case of cross-correlation wherein a signal is
correlated with a time-lagged version of itself - the resulting signal comprises only the
periodic information from the original signal whilst eliminating noise. This property of
autocorrelation can be particularly useful in analyzing bearing faults since vibration data
from a bearing, with local faults/defects, consists of cyclostationary acceleration signals
usually contaminated with noise from sensors and other environmental factors. This study
introduces a method which provides early failure warning in rolling element bearings by
applying an autocorrelation operation to vibration data. The Sequential Probability Ratio
Test (SPRT) is used to detect anomalies indicative of incipient failure. The results from the
autocorrelation analysis are compared with results from a simple moving-RMS analysis of
the acceleration data. The developed method is shown to provide an earlier warning of
failure than the RMS - based method. This method can detect early stages of degradation in
bearings - which in turn allows earlier scheduling of maintenance and the avoidance of
system failures