Location of Repository

Autoregressive based diagnostics scheme for detection of bearing faults

By Suguna Thanagasundram and Fernando Soares Schlindwein


Paper presented at the ISMA2006 Noise and Vibration Engineering Conference, held in Leuven, Belgium, from 18-20 September 2006. Conference program and proceedings available from http://www.isma-isaac.be/past/conf/isma2006/An investigation into the vibration characteristics of a ‘Roots and Claws’ based dry vacuum pump under different operating conditions was conducted. An AutoRegressive (AR)-based condition monitoring algorithm was developed and tested on both a fault-free and a pump with an implanted ceramic bearing with an inner race defect at the High Vacuum (HV) end. The investigation provided some in-depth\ud understanding of the effects of different operating conditions such as speed and load on the vibration of the pump. The first key step in the fault detection scheme was accurate determination of the running speed\ud of the pump. It was observed that the rotating speed of the pump’s rotor shaft on which the bearing case was directly connected to was often less than the set speed of the pump due to rotor slip. The second step was envelope demodulation of the time domain vibration signals where the resonance excited by the fault-induced\ud impacts was identified and the vibration signal were bandpass filtered around the resonant peak. The third step is spectral estimation using parametric-based method of AR modelling. The advantage of the AR method is that it can work with smaller sample sizes and sampling rates compared to the more traditional approach of FFT (Fast Fourier Transform) and achieve far superior resolution capabilities. The analysis results showed that the effect of actual speed was predominant in the detection of bearing faults as this was the speed that was used in the calculations of the bearing defect frequencies and had to be\ud determined very accurately. Initial results show that the fault diagnostic scheme is very promising and the bearing fault could be accurately determined at all speeds

Year: 2006
OAI identifier: oai:lra.le.ac.uk:2381/174

Suggested articles



  1. (2005). A case study of AutoRegressive Modelling and Order Selection for a Dry Vacuum Pump.
  2. (2000). A comparison of cyclostationary and envelope analysis in the diagnostics of rolling element bearings. doi
  3. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, doi
  4. (1997). An analytical model for the prediction of the vibration response of rolling element bearings due to a localized defect. doi
  5. (1998). Application of periodic time-varying autoregressive models to the detection of bearing faults. doi
  6. (1997). Bearing condition diagnostics via vibration and acoustic emission measurements. doi
  7. (1996). Comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings. doi
  8. (1978). Demodulated resonance analysis - A powerful incipient failure detection technique.
  9. (1985). Diagnostic Monitoring of Rolling-Element Bearings by HighFrequency Resonance Technique. ASLE Transactions, doi
  10. (1989). Dry pumps operating under harsh conditions in the semiconductor industry. doi
  11. (1998). Fault detection and monitoring of a ball bearing benchtest and a production machine via autoregressive spectrum analysis. doi
  12. (1985). Frequency domain methods for monitoring the condition of rolling element bearings. Mechanical Engineering Transactions - Institution of Engineers,
  13. (2001). High-resolution methods in vibratory analysis: Application to ball bearing monitoring and production machine. doi
  14. (1975). Linear Prediction: A Tutorial Review. doi
  15. (1993). Machine condition monitoring: part 1 - optimum vibration signal lengths. doi
  16. (1984). Model for the vibration produced by a single point defect in a rolling element bearing. doi
  17. (2000). Modelling of the spalled rolling element bearing vibration signal: an overview and some new results. doi
  18. (1993). Parametric spectral estimation to detect and diagnose faults in low speed rolling element bearings: Preliminary investigations. doi
  19. (1981). Spectrum analysis-a modern perspective. doi
  20. (1985). The vibration produced by multiple point defects in a rolling element bearing. doi
  21. (1985). Time domain methods for monitoring the condition of rolling element bearings. Mechanical Engineering Transactions - Institution of Engineers,

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