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A Modified Whiteness Test for Damage Detection Using Kalman Filter Innovations

  • D. Bernal
  • Y. Bulut
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Deviations from whiteness in the innovations of a Kalman filter indicate that the filter is not optimal for the given data. Accepting that the disturbances are stationary and white lack of optimality derives from the fact that the values of some parameters have changed between the time the filter was formulated and the present. The parameters that define the filter come from system properties and from the statistics of the disturbances. For the filter to perform effectively as a fault detector it is necessary to ensure that deviations from whiteness are not due to variations in the statistics of the noise. This paper examines the mathematical relation between the covariance function of the innovations and the changes in the disturbance statistics. It is shown that the effect of changes in the noise statistics on the discriminating metric can be minimized by shifting the range of lags for which the metric is evaluated. The fact that the modified whiteness test can retain adequate sensitivity to damage is illustrated numerically.

Keywords

Kalman Filter Covariance Function Damage Scenario Whiteness Test Kalman Filter Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Patton, R. J., Frank P. M., and Clark R. N. Issues of Fault Diagnosis for Dynamic Systems. Springer. 2000.Google Scholar
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    H. Heffes, “The effect of erroneous models on the Kalman filter response,” IEEE Transactions on Automatic Control, AC-11, July, 1966, pp. 541-543.Google Scholar
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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • D. Bernal
    • 1
  • Y. Bulut
    • 2
  1. 1.Civil and Environmental Engineering Department, Center for Digital Signal ProcessingNortheastern UniversityBostonUSA
  2. 2.Civil and Environmental Engineering DepartmentNortheastern UniversityBostonUSA

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