The Research On The Key Technologies In Acoustic Emission's Signal Processing

Acoustic emission (AE) is an important method of no-destructive measurement and evaluation, which is widely used in a number of fields, such as petrochemical industry, aerospace industry, material test, and transportation. The AE signal processing plays key role in the research of the AE measurement. According to the characteristic of the waveform, the AE signal can be divided into three types, i.e., burst signal, continuous signal and mixed signal. To study the characteristics and propagation mechanisms of different types of signals and find out their corresponding analysis methods is an important task to correctly inverse the information of the AE source.Based on both theoretical analysis and experimental study, signal preprocessing, feature extraction and information fusing methods for three types of AE signals are carefully studied from the aspect of signal processing. The main works and innovations of this dissertation are listed as follows:(1) The characteristics of three types of AE signals and their corresponding de-noise methods are studied separately. The burst signal can be decomposed into a modulation signal corresponding to group velocity and an exponential signal corresponding to phase velocity. Therefore, a new method which combines two types of methods, i.e., wavelet analysis and matrix pencil (MP) analysis, is introduced to denoise the burst signal. For the continuous signal, the frequency dispersion is relatively complicated and its mode number is large. The wavelet packet method based on the Shannon entropy rule is used to denoise the continuous signal. The mixed signal has distinctly different characteristics at different stages of the signal emission process, thus the key point of de-noise processing is to identify the different signal emission stages. Different methods, such as wavelet packet analysis and wavelet-MP-combined analysis are used at different stages of the signal emission process. The results show that the signal denoised by the methods introduced in this dissertation not only nicely keeps the information of the original signal but also obviously improves the SNR and reduces the RSME.(2) Due to the difficulty in estimation of the probability density function (PDF) of the AE signal, the factor-mixed ICA preprocessing is applied. An improved FastICA algorithm based on the empirical PDF is proposed. Compared with the general FastICA algorithms, the improved method has better convergence effect. The experimental results show that for the continuous AE signal generated from the water pipe leak the improved ICA method can effectively improved the location precision, i.e., the location precision is controlled under 3% for different leak positions.(3) Based on the Hurst exponent, a self-similar feature extraction method is introduced to analyze the AE signal of the concrete. Furthermore, a classification method is proposed to identify the stages of the AE process according to the average value and the variance of the Hurst exponent. We calculate the Hurst exponent for two types of concrete, i.e.. C50 and C60, and find there exists a transition phenomenon: for small stress the Hurst exponent decreases as the stress increases; it reaches a minimum at a certain critical point; then increases rapidly if we further increase the stress. This transition phenomenon is more obviously for the concrete with higher strength. The results show that the Hurst exponent based classification method has high precision for the identification of the unstable stage and is rarely affected by the experimental condition.(4) Based on the high-order spectral analysis, a non-Gaussian feature extraction method is also introduced to analyze the AE signal of the concrete. Another classification method is further proposed to identify the stages of the AE process according to the average value and the variance of the high-order spectrum. We study the characteristic of the bispectrum for two types of concrete, i.e., C50 and C60. The deviation from the Gaussion distribution increases as the stress increases. Especially for the status close to critical state of destruction, the average value of the bi-spectrum displays an increase of three orders of magnitude. The results show that the high-order spectrum based classification method is effective for identification of the initial stage and the stable stage.(5) A safety evaluation model of concrete based on Bayesian networks is proposed. The model applies an entropy-based discretization method to discrete the characteristic parameters. Using network measurement method and gradient decrease method, the structure and the parameters of Bayesian networks are established separately. The experimental results show that the Bayesian networks based evaluation method behaves more effectively and has a higher precision than other evaluation methods with singular parameter.