Condition Monitoring Using Periodic Time-Varying AR Models

  • Andrew C. McCormick
  • Asoke K. Nandi
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Abstract

Model based approaches to vibration monitoring can provide a means of detecting machine faults even if data is only available from the machine in its normal condition. Previous work has assumed stationarity in the vibration signals and exploited only time-invariant models. The cyclostationary nature of rotating machine vibrations can be exploited by using periodic time-varying autoregressive models to model the signal better than time-invariant models, and this can improve the fault detection performance. Experimental data collected from a small rotating machine set subjected to several bearing faults was used to compare time-varying and time-invariant model based systems.

Keywords

Cage Autocorrelation 

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Andrew C. McCormick
    • 1
  • Asoke K. Nandi
    • 1
  1. 1.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK

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