Wavelet And Fractal Signal Processing In Ultrasonic Testing Of Friction Welding

As a new advanced technology of solid state jointing, fraction welding has been used in aviation, spaceflight and other fields abroad. In the thesis, we investigate the application of wavelet transform and theory of fractal in fraction welding ultrasonic testing signals to identify the defects and classify with the ultrasonic signal detected from friction welding joints which is made from GH4169.Firstly, we discuss the selected topic and its background of the paper, and then go into the basic theory of wavelet transform. Aiming at the characteristic of friction welding ultrasonic testing signals and noise, we mainly focus on the selection of suitable wavelet bases and threshold, and confirming the divided layer number. We extract signals from the noisy signals successfully by wavelet transform, and prepare for the future.Then wavelet transform is used to study the ultrasonic and test signals' self-similarity characteristic. The result shows that they are fractal and can be characterized quantitatively by fractal dimension. So theory of fractal is introduced to analyze fraction welding ultrasonic testing signals;and box dimension is also introduced as the judgment of signal's complex degree. The box dimension of sin signal is used to verify the correctness of the method. We use box counting method to identify fractal scaleless band during the computing of fraction welding ultrasonic testing signals, do statistical analysis on box dimension of all signals, and find the relationship between box counting dimension and signal classes so that the defects can be detected and classified exactly.Finally, we use wavelet transform for fraction welding ultrasonic testing image edge detection, and approximately partition defects location, witch indicate wavelet transform is precede than tradition edge detecting methods.