Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing

Abstract

Spectral kurtosis (SK) is an algorithm that gives an indication of how kurtosis varies with frequency. A frequency band that contains abundant information, especially the impact signal, can be tracked by calculating SK. In the present article, SK combined with Autoregressive AR model, was applied into the fault diagnosis and condition monitoring of bearings. Accelerated life test of rolling bearings in Hangzhou Bearing Test & Research Center (HBRC) was performed to collect vibration data over their entire lifetime (normal-fault-failure). The result shows that SK can detect early incipient fault by eliminating some other interfering frequency components. In addition, it can detect fault 5 min earlier than root mean value (RMS). This fault detection in advance is significant for condition monitoring.