Study On Key Technologies Of Non-imaging Ultrasonic Defects Identification And Its Application

Defect identification is an important basic issue in quantitative ultrasonic nondestructive testing. With the development of ultrasonic testing towarding high reliability, high accuracy, real-time and quantitative analysis, it has greatly academic significance and engineering value to research on online ultrasonic defect identification. Although it is possible to achieve defect identification by ultrasound imaging method to synthesize geometric contour of the defect, but because of the time cost for the scan of whole object and the data synthesis, the imaging method can not meet the needs of high-speed online ultrasonic testing. Non-imaging ultrasonic defect identification method, which extracts features directly from ultrasonic signal and achieve defect identification by analyzing the relationship between features and defect types, does not need the time waiting for object scan and data synthesis, so, it is very suitable for online ultrasonic inspection and defect identification. However, there are still some problems in the practical application, such as the disturbance of grain noise, the shortage of priori knowledge in the small-sample situation, which can affect the accuracy and generalization of defects identification seriously. To solve these problems, some systematic studies are carried out on grain noise removal, feature extraction and pattern recognition, and the targeted methods are put forward, including a time-frequency adaptive de-noise method base on wavelet packet decomposition, a time-frequency features extraction method based on SFFS search algorithm, two fusion decision-making identification methods based on support vector machine.At last, tests are carried out with artificial defects and natural defects on oil casing pipe to verify the feasibility and effectiveness of these methods.In the first chapter, the importance of non-imaging ultrasonic defect identification is discussed, the research situation at home and abroad on the key technologies of non-imaging ultrasound defect identification is presented, and problems in current research are analyzed to guide the ways for further research work.In the second chapter, the composition, distribution and non-stationarity of ultrasonic signals are discussed, and the characteristics of ultrasonic signal achieved from typical artificial defect are analyzed at different space domains. These analysis results can provide a theoretical basis to noise removal, feature extraction and recognition in the follow-up section.In the third chapter, considering the distinctness of distribution between defect signal and noise, a time-frequency adaptive de-noise method base on wavelet packet decomposition is presented. Experiments are carried out with both simulated signals and real signals to verify the effectiveness of this method for the signal noise ratio improvement and the suppression of signal distortion.In the forth chapter, the multiple features extraction frame of ultrasonic signal is presented. Four irrelevant traditional feature extraction methods are discussed, and their implementations are presented. Considering the lack of quantitative criterion in traditional feature extraction methods, a new time-frequency feature extraction method based on wavelet decomposition, Fisher principle and SFFS selection algorithm is presented to optimize feature mining capability. The effectiveness of thees feature extraction methods are evaluated with distinguish ability criterion.In the fifth chapter, considering the small sample problem of defect identification in ultrasonic testing, two fusion decision-making identification methods are presented respectively for the applications where the defect type frame is known or unknown. A test with different artificial defects by ultrasonic inspection method is carried out to verify the effectiveness of these identification methods for the improvement of accurate and generalization.In the sixth chapter, theories and methods presented out in this paper are applied to the identification of natural defects on oil casing pipes. The influences caused by noise and feature extraction methods to the identification accurate are discussed. And the accurate and generalization of fusion decision-making identification methods are verified. At last the time costs of these methods are measured, and the feasibility for online ultrasonic testing is analyzed.