Studies On Integrated Platform For Acoustic Emission Signal Processing

Acoustic emission signal processing is the only effective method to acquire the acoustic emission source information by now and the integrated platform for signal processing became more important for the engineering application of acoustic emission technique. Aiming at the shortcoming of the products for signal processing platform developed by foreign companies and the domestic status of related research, an integrated platform for acoustic emission signal processing based on wavelet analysis and neural network has been developed in the thesis. The application of wavelet analysis on acoustic emission signal processing was studied in the thesis. At first, the rules of how to select the suitable wavelets for acoustic emission signal processing were proposed based on the features of acoustic emission signal. In terms of the rules, Daubechies wavelet, Symlets wavelet and Coiflets wavelet were regarded to be suitable for acoustic emission. Secondly, the frequency decomposition band of wavelet analysis was formulated and the maximum decomposition level of wavelet analysis was also formulated. The research results above were important to use wavelet analysis for acoustic emission signal processing. Three feature extraction methods for acoustic emission signal based on wavelet analysis were proposed: wavelet feature frequency analyzing method, wavelet feature energy frequency coefficient method and wavelet decomposition coefficient method. The results of practical engineering application showed that the three methods can efficiently extract the features of acoustic emission signal.The common problems confronted in the application of BP neural network on the pattern recognition of acoustic emission signal were studied. To improve the neural network performance, the thesis purposed such three methods as improved BP algorithm, noise-added repetitious training method, combination of wavelet analysis and neural network. Experimental results showed that BP neural network based on the three methods above can make better results for the pattern recognition of acoustic emission signal.Based on the research results above, the thesis developed the first integrated platform for acoustic emission signal processing in China. As the core module, the platform has been integrated into the first multi-channel waveform digital acoustic emission instrumentation in China. By now, the platform has been applied in many engineering fields related to acoustic emission technique and made better results.For the first time, blind deconvolution was used to research acoustic emission source signal in the thesis. The acoustic emission source signal and the impulse response function of transmission path were estimated at the same time. Experimental results showed that blind deconvolution can recover the simulated acoustic emission source signal and especially can recover the source information of acoustic emission distorted by amplitude saturation to some extent. The research work explored one new orientation for the research of how to recover the acoustic emission source signal from the acoustic emission signal.The research results of the thesis have important significance and practical value to promote the development of acoustic emission technique and to improve the feature information extraction of acoustic emission source. The research work of the integrated platform for acoustic emission signal processing established the stable basis of successful development of the first waveform digital acoustic emission instrumentation in China.