Fault detection in bearings using autocorrelation

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 reducing noise. This property of autocorrelation can be particularly useful in analysing 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.