Signal Processing Tools for Tracking the Size of a Spall in a Rolling Element Bearing

  • R. B. RandallEmail author
  • N. Sawalhi
Conference paper
Part of the IUTAM Bookseries book series (IUTAMBOOK, volume 1011)


There is considerable interest in diagnostics and prognostics of operating machines based on vibration analysis and signal processing, because the major economic benefit from condition-based monitoring comes from being able to predict with reasonable certainty the likely lead time before breakdown. In the case of rolling element bearings, a number of powerful techniques have been developed in recent years to separate the rather weak signals coming from faulty bearings from strong background vibrations, and to diagnose the type of fault. The MED (minimum entropy deconvolution) technique was initially applied to bearings to reduce the overlap of adjacent impulse responses in high speed bearings and thus allow their diagnosis by envelope analysis. It was then suspected that the technique also might have the potential to separate the impulses from entry into, and exit from an individual fault, and thus give information on the fault size. This paper gives the results of an initial study into the application of MED, and other techniques, to obtain the best measure of the length of a developing spall, to use in prognostic algorithms to estimate safe remaining life, based on current size and rate of evolution with time. It was found that the response to the entry and exit events was markedly different, so considerable pre-processing was required before the MED could be applied. The paper also discusses a number of methods to reduce noise and obtain an averaged estimate of the spall length.


Bearing diagnostics Bearing prognostics Fault size determination Vibration analysis Machine condition monitoring 



This research was supported by the Australian Defence Science and Technology Organisation (DSTO) through the Centre of Expertise in Helicopter Structures and Diagnostics at UNSW.


  1. 1.
    Sawalhi, N., Randall, R.B.: Semi-Automated Bearing Diagnostics – Three Case Studies. Comadem Conference, Faro, Portugal, June (2007)Google Scholar
  2. 2.
    Darlow, M.S., Badgley, R.H., Hogg, G.W.: Application of high frequency resonance techniques for bearing diagnostics in helicopter gearboxes, US Army Air Mobility Research and Development Laboratory, Technical Report: 74–77 (1974)Google Scholar
  3. 3.
    Ho, D., Randall, R.B.: Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech. Syst. Signal Process. 14(5), 763–788 (2000)CrossRefGoogle Scholar
  4. 4.
    Antoni, J., Randall, R.B.: The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. 20(2), 308–331 (2006)CrossRefGoogle Scholar
  5. 5.
    Antoni, J.: Fast computation of the kurtogram for the detection of transient faults. Mech. Syst.Signal Process. 21, 108–124 (2007)CrossRefGoogle Scholar
  6. 6.
    Wiggins, R.A.: Minimum entropy deconvolution. Geoexploration, Elsevier Sci. Publ. 16, 21–35 (1978)CrossRefGoogle Scholar
  7. 7.
    Sawalhi, N., Randall, R.B., Endo, H.: The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech. Syst. Signal Process. 21, 2616–2633 (2007)CrossRefGoogle Scholar
  8. 8.
    Dowling, M.: Application of non-stationary analysis to machinery monitoring. IEEE paper, 0-7803-0946-4/93, 159–162 (1993)Google Scholar
  9. 9.
    Epps, I.K., McCallion, H.: An Investigation into the Characteristics of Vibration Excited by Discrete Faults in Rolling Element Bearings. Annual Conference of the Vibration Association of New Zealand, Christchurch (1994)Google Scholar
  10. 10.
    Sawalhi, N., Randall, R.B.: Spectral Kurtosis Enhancement using Autoregressive Models. ACAM Conference, Engineers Australia, Melbourne (2005)Google Scholar
  11. 11.
    Randall, R.B.: Frequency Analysis, 3rd edn. Bruel & Kjaer, Naerum, Denmark (1987)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.School of Mechanical and Manufacturing EngineeringThe University of New South WalesSydneyAustralia

Personalised recommendations