Study On Ultrasonic Signal Sparse Deconvolution And Sparse Compression Methods For Pipeline Flaw Inspection

Currently, offshore pipeline ultrasonic detection technology is a research hotspot in international non-destructive testing field. It has great theoretical and realistic sense to research the technologies, methods, and approaches for pipeline detection. Based on "863" of the high technology research and development program "Offshore pipeline detection device and inspection technology" and National Natural Science Foundation of China "Sparse compression for ultrasonic signal and its hardware implementation", according to the detection requirements of offshore pipeline, the sparse component analysis method have been used to study the theory and techniques of flaw detection and mass ultrasonic data compression deeply, such theory and technologies explored in the dissertation can provide key technologies for the data analysis system of the pipeline intelligent detection device.Firstly this dissertation studied the theory of sparse component analysis method, and the exact recovery conditions and astringency have been explored emphatically. Based on the properties of sparse component analysis method, a new sparsity measure function was constructed with better robustness, and the related properties of such function has been given and proved. Such function can provide the essential preliminary knowledge for ultrasonic signal sparse decomposition and sparse compression.Based on the properties of ultrasonic echo and the construction of the over-complete dictionary, a simplified over-complete dictionary has been provided. According to the structure of over-complete dictionary, the concept of inner cumulative coherence and outer cumulative coherence have been defined, after analysis the properties of this two concept, the over-complete dictionary aggregate divisional theorem was put forward and proved., and based on such theorem, a novel fast batch MP method has been developed which can attain the same effect as MP method, but the computational complexity with the proposed method is greatly reduced. At the same time, the residual ratio iteration termination condition for fast batch MP method has been developed, it can erase the disadvantage of the traditional termination condition which can not terminated in terms of an ultrasonic signal with an extremely low SNR. Such fast batch MP method is fast enough to be implemented in a real-time system.The received signal in ultrasonic pulse-echo inspection can be modelled as a convolution between an impulse response and the reflection sequence that is the impulse characteristic of the inspected object. In order to improve the time resolution so that the overlap between echoes from closely spaced reflectors becomes small, this dissertation presents a non-linear minimum entropy deconvolution algorithm that is robust to deconvolution ultrasonic signals. The robustness is obtained by including a non-linear function which can increase the sparsity of the iteration output and decrease the influence of the added noise. Meanwhile, based on the study of sparse decompose method, the dissertation presents an innovation weighted iteration sparse deconvolution algorithm and the theoretical investigation of the proposed algorithm principle, the algorithm is very flexible for its application. The two deconvolution algorithm can not only calculates the thickness of the pipeline wall accuracy, but also detect the flaw in pipeline with high precise, especially the near surface flaw which the traditional method can not. The experiment results show the weighted iteration sparse deconvolution algorithm can take a role of filtering that is not the same comparing to the normal filter method, it can show the intrinsic character of the ultrasonic signal, and reflect the intrinsic property of each echo wave.Based on the properties of ultrasonic echo, a simplified over-complete dictionary in which some atoms is ultrasonic wavelet has been provided, and the sparse compression algorithm was presented for ultrasonic signal compression. Because the ultrasonic wavelet is the approximation of the ultrasonic echo, the ultrasonic signal has a sparsest representation, and the sparsity of weight vectors attained by decomposing the ultrasonic signal using the sparse compression algorithm is very big. The energy of the sparse weight vectors is highly centralized, only a limited number of non-zero weight vectors can reconstruct the ultrasonic signal with a minimal loss in signal quality. Comparing with the matching wavelet compression algorithm, the sparse compression algorithm is capable of compressing the ultrasonic signal at higher compression rates,smaller root-mean-square error,minimal loss in signal quality and higher ratio of arithmetic mean to geometric mean. For compressing the ultrasonic signal, the sparse compression algorithm can achieve the biggest compression rates about 200:1 with virtually no loss in signal quality. Because the amount of the atoms in the over-complete dictionary is very small, it is fast enough to be implemented in a real-time data compression system. Because the ultrasonic wavelet is the approximation of the ultrasonic echo, the sparse compression algorithm can not only give better sparse representations and better compression results, but also give excellent performance for feature extraction and ultrasonic signal denoising.