Feature Extraction of Bearing Vibration Signals Using Autocorrelation Denoising and Improved Hilbert-Huang Transformation

Ya-Juan Xue; Jun-Xing Cao; Ren-Fei Tian; Qing Ge
March 2012
International Journal of Digital Content Technology & its Applic;Mar2012, Vol. 6 Issue 4, p150
Academic Journal
In order to extract the fault feature frequency of weak bearing signals, an Hilbert-Huang transform(HHT) method combining with autocorrelation transform is put forward in this study. After performing autocorrelation denoising, the empirical mode decomposition(EMD) method was used to decompose the signals into a number of intrinsic mode functions(IMFs). Energy ratio of the each IMF's energy to the original signal energy was calculated to select a valid set of IMF by selecting the appropriate threshold. And the marginal spectrum was obtained to detect bearing fault information and identify the fault patterns. This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.


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