For gears and roller bearings, periodic impulses indicate that there are faults in the components. However, it is difficult to detect the impulses at the early stage of fault because they are rather weak and often immersed in heavy noise. Existing wavelet threshold de-noising methods do not work well because they use orthogonal wavelets, which do not match the impulse very well and do not utilize prior information on the impulse. A new method for wavelet threshold de-noising is proposed in this paper; it not only employs the Morlet wavelet as the basic wavelet for matching the impulse, but also uses the maximum likelihood estimation for thresholding by utilizing prior information on the probability density of the impulse. This method has performed excellently when used to de-noise mechanical vibration signals with a low signal-to-noise ratio.

Issue Section:
Technical Papers
Keywords:
wavelet transforms, acoustic signal processing, machine bearings, maximum likelihood estimation, signal processing, Gaussian noise, vibrations
Topics:
Impulse (Physics), Maximum likelihood estimation, Noise (Sound), Signals, Wavelet transforms, Wavelets, Flaw detection, Vibration, Roller bearings
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