Research On Denoising Methods Of Ultrasonic Testing Signal Based On Wavelet Transform

In practical use, the ultrasonic testing signal received not only includes much useful information, but also has various interference signals (these are noises) in it, and the interference signal affects the real ultrasonic testing signal heavily, which goes against the analysis and farther treatment of ultrasonic testing signal. Therefore, it is quite necessary and important that eliminating or decreasing noise during the pretreatment of ultrasonic testing signal.Wavelet analysis is internationally acknowledged high technology in the fields of information obtained and processed at present, and it is the forward topic of signal processing, also it is used widely in practical. Based on the wavelet theory, this article proposed several new wavelet denoising methods, which were used in the denoising area of ultrasonic testing signal, thus the application range of wavelet was widen. The main works of this article are as follows:1. The wavelet threshold denoising methods were discussed emphatically, and the characteristics of two traditional wavelet shrinkage methods, which referred to soft-threshold and hard-threshold respectively, were analyzed in wavelet domain, then a modified compromise threshold method was proposed on the basis of the standard compromise threshold method. Compared with the standard compromise threshold method, the threshold function of the modified method has more flexible form, which is convenient for several kinds of mathematical disposals; compared with the two traditional wavelet shrinkage methods, it overcomes the shortcoming of discontinuity of hard-threshold and decreases the fixed bias between estimated wavelet coefficients and decomposed wavelet coefficients of soft-threshold. The simulation results show that the modified method is better than the methods, which contain soft-threshold method, hard-threshold method and the standard compromise threshold method.2. After studied the characteristics of Particle Swarm Optimization, Particle Swarm Optimization was used in wavelet domain to optimize the thresholds, and garrote shrinkage function was used to process wavelet decomposition coefficients, which overcame the shortcoming of discontinuity of hard-threshold and decreased the fixed bias between estimated wavelet coefficients and decomposed wavelet coefficients of soft-threshold. The simulation results show the validity of this new method.3. Introduce the basic theories of wavelet packet analysis in detail. And according to the feature that the wavelet coefficients of signals and noises on each scale had different characteristic, SURE threshold, Bayesian threshold, fixed form threshold and the moving minimax threshold were combined during wavelet packet threshold de-noising. Simulation results show that the proposed method indeed has better de-noising effect.In a word, study basic theories of wavelet, and apply into new field with improved methods. This topic has important theoretical significance and practical value.