Application of periodic time-varying autoregressive models to the detection of bearing faults

Abstract

Model-based approaches to vibration monitoring can provide a means of detecting machine faults even if data are only available from the machine in its normal condition. The cyclostationary nature of rotating machine vibrations can be exploited by using periodic time-varying autoregressive models to model the signal better than time-invariant models. Experimental data collected from a small rotating machine set subjected to several bearing faults were used to compare time-varying and time-invariant models. Comparison is also made with a simple feature-based neural network fault diagnosis system.