| Summary: | In practical applications, the signal measured from a complex mechanical system is usually disturbed by various noises due to the compounded effect of interferences of other machine elements and background noises, especially under varying speed conditions. Resonance-based approaches have been proven to be effective methods to address this problem. However, even if the optimal resonance band is accurately determined, the in-band noise with frequency content in the range covered by the band-pass filter is not eliminated. To avoid missed diagnosis and misdiagnosis of faults in bearings, an iterated SVD (ISVD)-based in-band noise reduction method combined with envelope order spectrum analysis is proposed in this paper. First, the optimal frequency band of a vibrational signal is determined with the help of an enhanced wavelet packet transform kurtogram, in which the kurtosis of each node is calculated based on the envelope spectrum of a signal be reconstructed using the wavelet packet coefficients. The node with the maximum kurtosis value is used to reconstruct the signal. Second, the envelope of a reconstructed signal is calculated by Hilbert transform and the ISVD method is applied to it to reduce the in-band noise. To avoid the destruction of useful information caused by excessive iteration, a threshold is set to determine the number of iterations. After iterative processing, a de-noised signal is reconstructed based on the relationship between the singular value and a frequency component. Finally, the reconstructed signal is resampled and transformed into the fault characteristic order domain where the bearing fault type can be identified from the envelope order spectra. The simulations and experiments were used to validate the efficacy of the proposed method. Compared with the spectral subtraction method, the ISVD method can suppress in-band noise efficiently and beneficial to extract the fault characteristic order under variable speed conditions.
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