Industrial Signal Filter Base on Wavelet Transform

Since some industrial signals have very low sampling rate, it is impossible to remove the noise from this kind of signal via traditional filter method. This work carefully introduced the wavelet transform de-noise method, including the practical filter bank way to perform the wavelet transform and inverse wavelet transform. The programming work flows of doing signal filtering via wavelet transform using Python also presented. After comparing the two wavelet transform de-noise methods via denoising artificial signals and real industry signals of circulating fluidized bed (CFB), the characteristics of this two methods are summarized and some suggestions on the industrial signals de-noise via wavelet transform are presented.