The Denoising Method for Bearings Based on the Improved Threshold Principle and Sample Entropy
In order to improve the noise reduction effect of fault bearing signals and reduce the proportion of noise signals in reconstructed signals,a wavelet threshold noise reduction algorithm based on improved threshold principle combined with sample entropy is proposed.The signal is decomposed into multiple layers by wavelet transform,and the thresholds of different decomposition layers are set using the improved threshold principle based on sample en-tropy.Finally,the wavelet coefficients after noise reduction are reconstructed to achieve the final noise reduction of the signal.The results of simulation show that the wavelet threshold noise reduction method based on improved threshold principle can effectively denoise bearing signals,and the noise reduction effect is better than the tradition-al general threshold principle and fixed threshold principle.
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