Improved multi-wavelet denoising with neighboring coefficients of cutting force for application in the load spectrum of computer numerical control lathe

To obtain accurate computer numerical control lathe cutting force signals and improve the precision of load stress cycle statistic, an improved multi-wavelet denoising with neighboring coefficients method is proposed. First, statistical variance smoothing is applied to remove the singular points in the original signal. The processed signal is then denoised with the multi-wavelet with neighboring coefficients method. Second, based on the change laws of the correlation dimension and the values of Brock, Dechert, Scheinkman statistic of load signal, reasonable decomposition levels of the multi-wavelet and the length of neighboring coefficients are used. Third, four synthetic signals with different signal-to-noise ratios are denoised with the wavelet threshold denoising method and improved multi-wavelet denoising with neighboring coefficients. Then, the difference between the values of correlation dimension and Brock, Dechert, Scheinkman statistic in the original and denoised signals is analyzed. Meanwhile, its validity is further verified with the signal-to-noise ratio and mean square error. The results show that the improved multi-wavelet denoising with neighboring coefficients is better than wavelet threshold. Finally, turning force signals are denoised by wavelet threshold and improved multi-wavelet denoising with neighboring coefficients. Comparison result shows that the improved multi-wavelet denoising with neighboring coefficients can not only remain largely low-frequency signal energy and suppress high-frequency noise signals effectively but also improve the accuracy of load stress cycle statistic.