Blasting Vibration Monitoring Data Based On Wavelet Denoising

With extensive application of blast techniques, more and more attentions are focused on the influence of blast vibration. The blast vibration damage has become an important problem of environmental protection in economic development. We have to monitor and control the blasting process so as to cut down blast vibration damage. The theorems of monitor methods are to analyze characteristic of propagation of blast vibration elastic wave so as to investigate the influences to other erections .Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Therefore, collecting these "accurate and real" data is very important .However ,as a result of measurement inaccuracy, precision of instrument and calculation inaccuracy, the data we get usually include different noises that can not smooth away easily ,at the same time ,these noises can frequently swallow up the real signals . These noises' existence can influence the interpretation of the blast vibration analysis . so , how to clear up these noises which lie in data and abstract these "accurate and real" data are to be a key technique of data processing.To those stationary signals, adopting Fourier transform to eliminate noises will achieve relatively ideal effects, but Fourier transform has itself limitation , that is, it cannot reflect the details of signals on an field of time-frequency, that can make choose frequency band inproperly during the processes of eliminating noises and the results not very perfect as you wish. In relation to different part of frequency, the wavelets transform shows characters of different time resolution, which make it suitable for analyzing and processing non-stationary signals.Spatially Selective Noise Filtration method is better than Fourier transform de-noising method ,but it has large comuputation trask, and maybe take error signal in.Donoho threshold method can help us get approximately perfect estimation of the most original signals,meanwhile, It needs not so much computation as SSNF method. The wavelet pocket transform divide the wavelet pocket more fme,and let wavelet pocket has better ablity to Time-Frequency analysis, especially , as for blast vibration signal that is no-stationary signal including lots of high,midum frequency ,it can better analysize in TF local field. So the wavelet pocket threshold de-noising method is better than wavelet threshold de-noising method in the asfact of noise filtration ; but for the blast vibration signals, the traditional threshold method we apply to eliminate the noises can bring forth the phenomenon of Psuedo—Gibbs. Translation invariance wavelet transform de-noising method can not only eliminate effectively the phenomenon of Psuedo—Gibbs during the blast vibration signals, but also decrease the weighted mean square error(MSE). This passage adopts translation invariance wavelet transform de-noising method to de-noising. The theory of Translation invariance wavelet transform de-noising method is as fllowed: First, Perform the cycle-spinning for the signal to be de-noised , and then, the soft/hard threshold was used to shrink the wavelet coefficient of the signal and reconstruct the signal, next, the signal is performed inverse cycle-spinning. Though several times spinning—de-noising—spinning and averaging the result, spinning dependence of wavelet basis can be removed. The method can suppress pseudo-Gibbs phenomena on the singularity points of signal produced by de-noise algorithm based on wavelet shrinkage, while , It can get ride of noise effectively.This paper proves that the effet of de-noising methods basing on wavelets has advantages over traditional de-nosing methods through theory,signal-modeling and the blast engineering case of hydorlic power plant.This paper show that the de-noising methods basing on wavelet has feasibility by signal modelling and the blast vibration monitor engineering case in tuoxi hydrolic power plant ,at the same time, also proves that the TI method has priorities over the SSNF method and threshold method.