Acoustic Emission Signal Processing System And Source Recognition Methods

Acoustic emission (AE) is an important non-destructive testing technology and its major objective is to locate and identify AE source in order to detect the damage degree and the service life of testing objects. AE signal analyzing and processing is the only way to achieve this goal. Research about AE signal processing system with higher performance and more effective AE sources identification method which helps to improve the identification, assessment and positioning accuracy of AE source has important theoretical significance and practical value.At present, with the AE detection range expanding and test object more diversification, the traditional AE signal processing techniques and testing instruments based on the parameters analysis hardly improve the ability of resolution, filtering and classification. Thus waveform analysis technology became a new research direction and research emphases of the AE signal processing. However the signal processing techniques based on waveform analysis are the difficulty and bottleneck of AE testing because the AE signal is a kind of weak signal hidden in the strong background noise which has the characteristic such as nondeterminacy, unpredictability, transient and multiformity. Combined with the characteristics of AE signal, new information processing technologies such as the wavelet analysis and support vector machine (SVM) model identification are introduced into the AE signal processing field to solve the problem above. The research focus on such key technical problems as processing system hardware and software design, filtering processing, time delay estimation, source location and classification.1. High-speed multi-channel acoustic emission signal processing system design based on the waveform analysisAiming at the shortcoming of existing AE signal processing system in improving AE testing performance, a high-speed multi-channel acoustic emission signal processing system and its software and hardware function module were designed with the general and expansibility.Based on PCI bus data communication mode between DSP and computer, the acoustic emission data acquisition and pretreatment system were designed by using DSP and FPGA embedded hardware structure. Various functions such as acoustic emission data analysis, display and output were realized by using powerful display function and rich software programming resources of the computer system. The system satisfies the requires of high speed and large amount of calculation for acoustic emission signal processing. It improved the acoustic emission signal acquisition and processing precision and provided a complete software and hardware platform for subsequent research work.2. The study of acoustic emission signal de-noising method based on wavelet analysisAfter studying denoising methods and steps based on wavelet analysis, three denoising methods including wavelet modulus maxima algorithm, wavelet scales related law algorithm and wavelet threshold method were compared for their quality and applicable conditions. Then the wavelet threshold denoising method was identified as the key research content for its simple structure and small amount of calculation.After analyzing the key technical issues about the threshold function selection and wavelet threshold optimization, comparative characteristics between the soft and hard thresholding function was studied and a half soft thresholding function was adopted for denoising. The signal-to-noise ratio and minimum mean square error were used as denoising effect evaluation index. The denoising effects of Wavelet functions including db6,sym8 and coif5 were analyzed through computer simulation experiment.Aiming at the shortcoming of traditional wavelet de-noising methods like poor flexibility, slow speed and hardware implementation difficulties, and a denoising method based lift wavelet was proposed. The denoising method was improved by using adaptive lifting schem and combined with the half soft thresholding function for AE signal denoising. And the results of the improved method are compared with that of the traditional wavelet transform and the lift wavelet transform. The simulated results showed that the adaptive lifting scheme had the best denoising results and has the advantages of simple structure, fast speed and easy hardware implementation.3. The study of cross correlated time delay estimation method based on wavelet analysisAfter studying the principle and characteristics of time difference location method for acoustic emission source, the influence of time delay estimation for positioning accuracy was analyzed. And a cross correlated time delay estimation method based on wavelet transform is proposed. The wavelet analysis is combined with the correlation analysis in the proposed method.From the wavelet transforming of acoustic emission signal, theory formula of the proposed method was deduced. Considering the nonstationary random characteristic of AE signal, the cross correlated time delay estimation algorithm based on the Coif5 wavelet is proposed after analyzing of the orthogonality, symmetry and smoothness and regularity of the wavelet function. The proposed algorithm was compared with the original correlated time delay estimation algorithm by computer simulation analysis under the condition of different noise. The results showed that. the proposed algorithm reduced the impact caused by the acoustic emission wave frequency dispersion and the noise, and improved the positioning accuracy without restriction of the signal noise correlation.4. Research and Improvement of AE time difference location methodThe propagation properties analysis of AE signal in composite materials showed that the sound velocity was very different at different direction of propagation because of the non-uniformity and anisotropic. But the sound velocity is set for a constant in the existing time difference location methods, which resulted in low positioning accuracy when the method was used for processing AE signal in composite materials. Even the method can not work under the condition with high frequency of AE event, serious transmission attenuation or limited inspection channels.Combining the time difference estimation method based on wavelet transform, an improved time difference location method was proposed to solve the problem. The influence caused by the sound velocity difference was eliminated by using a sound velocity- angle relationship equation. Comparative experiments of the time difference location method and its improved method were completed on the Carbon fiber composite material plate. The results showed that the proposed method improved the acoustic emission source location accuracy effectively, and the positioning error within 3%.5. Study of AE source classification method based on SVMAfter studying the existing AE signal classification method by analyzing the advantages and disadvantages, a classification method for AE source based on SVM is proposed. The limitations of the existing classification methods for AE signal include that researchers need to have rich background knowledge and data analysis experience for the method such as amplitude identification, frequency identification and statistical pattern recognition, and fuzzy diagnosis and artificial neural network method relies on statistical characteristics under the large sample data set. But in practical application, acoustic emission signal has irreversibility that result in the acoustic emission signal of pattern recognition usually does not have large amounts of sample data. The contradiction causes that the methods above have the problem such as getting in local minimization and poor generalization ability which restrict the acoustic emission sources identification accuracy improvement.After studying the application of wavelet packet analysis in signal processing, this paper proposed a wavelet packet-based characteristic parameter extraction method for AE signal. Combing the method with the multi-classification methods of SVM, the AE signals caused by the damage modes including fiber fracture, matrix cracking and interface separation were classified. The experiment result proved the feasibility of the AE source classification method based on SVM, and the identification accuracy effectively was effectively improved.