Method of De-Noising By Spectral Subtraction Applied to the Detection of Rolling Bearings Defects

Abstract

In this paper we aim to show the significance of spectral subtraction for the improvement of the sensitivity of scalar indicators (crest factor, kurtosis) within the application of conditional maintenance by vibratory analysis on ball bearings. If we consider the case of a bearing in good condition of use, the distribution of the amplitudes in the signal is Gaussian. When the bearing is damaged, the appearance of spallings disturbs this signal, modifying this distribution. This modification goes through the presence of periodical impulses produced each time a rolling element meets a discontinuity on its way. Nevertheless, the presence of background noise induced by random impulse excitations can have an influence on the values of these temporal indicators. The de-noising of these signals by spectral subtraction in different frequency bands allows us to improve the sensitivity of these indicators and to increase the reliability of the diagnosis.

Keywords

Spectral subtraction crest factor kurtosis

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