Study On The Preprocessing And Analysis Of Crh Data Based On Wavelet

With the development of the railway informationization, data analysis will play a more important role in assuring the safe and comfort operation of the CRH for the development of the national economy. However, due to the complex environment and so on, the collected data is mingled with noise. The noise has a direct and big effect on the result of data analysis on CRH data. Therefore, it is of great practical significance to research on denoising technology for CHR data.In these denoising technologies for CRH data, most of the traditional denosing technologies are based on the Fourier Transformation and statistics to deal with the noise. Because the methods based on statistics are subjective and mandatory, the denoising result is not good and flexible. The ones based on Fourier Transformation have not good denoising effects for the time-frequency unitary of Fourier Transformation, especially for the nonstationary and other complex signals. The wavelet transform has developed into a new science from the late 1980s. Because of its flexible and strong time-frequency localization property, it has been used for nonstationary and other complex signals widely and successfully.Firstly, the paper starts with the basic theory of wavelet analysis and introduces the denoising theory and common methods thoroughly. Following that, the parameters selection problem of wavelet threshold denoising is mainly researched on. Considering the special requirements of data analysis on CRH data, a series of experiments are conducted on the real data to deal with the parameters selection problem. The results of the experiments show that the selected parameters are objective and feasible. Secondly, for detecting the tunnel waveforms, a waveform detection algorithm is proposed on the basis of the denoising work for the light data. Finally, after analyzing the tunnel data, some useful information is showed for us.