Informative frequency band selection in the presence of non-Gaussian noise – a novel approach based on the conditional variance statistic with application to bearing fault diagnosis

Abstract In this paper a novel approach to local damage detection in the presence of non-Gaussian impulsive noise is introduced. The proposed approach is applied to bearing damage detection in technological processes of ore defragmentation. From the signal processing perspective, this corresponds to the identification of cyclic and non-cyclic impulses in the vibrations. A new informative frequency band selector based on the conditional variance statistic is proposed and studied in details. In particular, it is shown that the proposed method is superior to many common alternatives based e.g. on Kurtosis or Alpha selectors, especially when non-cyclic impulses dominate over the cyclic ones. Moreover, it is shown that the approach based on conditional variance is simple to implement and much more robust concerning various problem specifications like cyclic to non-cyclic impulse number ratio or amplitude ratio. The Monte Carlo method is used to validate the robustness of the method in the statistical sense.

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