Abstract
Extracting symptom of bearing faults from noisy signal is a key problem to detect early faults of machinery. An effective method is presented for improving the signal to noise ratio by the wavelet transforms. The paper, which is Part I of a pair, describes wavelet de-noising algorithms and their properties. The relationship between the thresholds for wavelet coefficients and the ability of wavelet de-noise under Gaussian noise is discussed. The advantage of the wavelet de-noising procedure on simulated data is illustrated. Simulated results have shown that using wavelet de-noising procedure is an effective means to extract the signal from noisy data.
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