Location of Repository

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

By Suguna Thanagasundram, Sarah K. Spurgeon and Fernando Soares Schlindwein

Abstract

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

Topics: Dry vacuum pumps, Bearing defects, Autoregressive modelling, Pole tracking, Fault detection
Publisher: Elsevier
Year: 2008
DOI identifier: 10.1016/j.jsv.2008.03.044
OAI identifier: oai:lra.le.ac.uk:2381/9036
Journal:

Suggested articles

Preview


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.