Abstract
A new scheme is proposed that combines autoregressive (AR) modelling techniques and pole-related spectral decomposition for the study of incipient single-point bearing defects for a vibration-based condition monitoring system. Vibration signals obtained from the ball bearings from the high vacuum (HV) and low vacuum (LV) ends of a dry vacuum pump run in normal and faulty conditions are modelled as time-variant AR series. The appearance of spurious peaks in the frequency domain of the vibration signatures translates to the onset of defects in the rolling elements. As the extent of the defects worsens, the amplitudes of the characteristic defect frequencies’ spectral peaks increase. This can be seen as the AR poles moving closer to the unit circle as the severity of the defects increase. The number of poles equals the AR model 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 characteristic bearing defect frequencies that are termed the ‘critical poles’ and are tracked for quantification of the main spectral peaks. The time-varying distance, power and frequency components can be monitored by tracking the movement of critical poles. To test the efficacy of the scheme, the proposed method was applied to increasing frame sizes of vibration data captured from a pump in the laboratory. It was found that a sample size of 4000 samples per frame was sufficient for almost perfect detection and classification when the AR poles’ distance from the centre of unit circle was used as the fault indicator. The power of the migratory poles was an alternative perfect classifier, which can be used as a fault indicator. The analysis has been validated with actual data obtained from the pump. The proposed method has interesting potential applications in condition monitoring, diagnostic and prognostic-related systems.
Highlights
Bearing failures are one of the most common reasons for breakdown of rotating machines in industry today
Their model takes into account the impulse series generated by a point defect in a bearing modelled from first principles as a function of the rotation and geometry of the bearing, the modulation of the periodic signal caused by nonuniform bearing load distribution, the transfer function of the vibration transmission from the rolling element bearing to the transducer, as well as the exponential decay of vibration
We present a study of fault identification through differences in the behaviour of the AR poles for vibration signals collected from two similar high speed dry vacuum pumps, one with a healthy set of bearings and another with a ball bearing with an inner race defect
Summary
Bearing failures are one of the most common reasons for breakdown of rotating machines in industry today. One of the possible approaches to fault monitoring of the bearings is the processing of vibration signals obtained from the external housings in which the bearings are mounted for extraction of diagnostic features [5] This technique is more commonly known as vibration signature analysis and there are many conventional procedures based on time harmonic and power spectrum analysis that have shown considerable success in detecting failures in machine components [6, 7]. If ball bearing defect frequencies [3] such as BPFO (Ball Pass Frequency of Outer Race), BPFI (Ball Pass Frequency of Inner Race), BSF (Ball Spin Frequency) and FTF (Fundamental Train Frequency, known as Cage Frequency) are to be detected, FFT spectra are computed and the spectra are filtered to monitor the presence of the fault frequencies Such a process can firstly be time consuming as whole frames of data have to be estimated.
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