A fault detection tool using analysis from an autoregressive model pole trajectory

摘要

A new scheme is proposed that combines autoregressive (AR) modelling techniques and pole-related spectral\uddecomposition for the study of incipient single-point bearing defects for a vibration-based condition monitoring system.\udVibration signals obtained from the ball bearings from the high vacuum (HV) and low vacuum (LV) ends of a dry vacuum\udpump run in normal and faulty conditions are modelled as time-variant AR series. The appearance of spurious peaks in the\udfrequency domain of the vibration signatures translates to the onset of defects in the rolling elements. As the extent of the\uddefects worsens, the amplitudes of the characteristic defect frequencies’ spectral peaks increase. This can be seen as the AR\udpoles moving closer to the unit circle as the severity of the defects increase. The number of poles equals the AR model\udorder. Although not all of the poles are of interest to the user. It is only the poles that have angular frequencies close to the\udcharacteristic bearing defect frequencies that are termed the ‘critical poles’ and are tracked for quantification of the main\udspectral peaks. The time-varying distance, power and frequency components can be monitored by tracking the movement\udof critical poles. To test the efficacy of the scheme, the proposed method was applied to increasing frame sizes of vibration\uddata captured from a pump in the laboratory. It was found that a sample size of 4000 samples per frame was sufficient for\udalmost perfect detection and classification when the AR poles’ distance from the centre of unit circle was used as the fault\udindicator. The power of the migratory poles was an alternative perfect classifier, which can be used as a fault indicator. The\udanalysis has been validated with actual data obtained from the pump. The proposed method has interesting potential\udapplications in condition monitoring, diagnostic and prognostic-related systems.