Research On Flaw Detection Technology Of Ultrasonic Phased Array For Oil And Gas Pipeline Girth Welds

Ultrasonic testing is an important flaw detection technology of oil and gas pipeline girth welds. With its unique electronic scanning, dynamic deflection focusing (DDF) and sectorial scanning characters, ultrasonic phased array (UPA) technology can be used for defect detection of curved face objects or objects with complex structure. Thus, UPA technology has been becoming a focus of ultrasonic testing research. Flaw detection and flaw identification of oil and gas pipeline girth welds, using UPA system are deeply researched in this dissertation.Based on rectangular plane piston array element, numerical analysis method is applied to research on the ultrasonic field characteristics of linear UPA. The deflecting and focusing effects of some array parameters, such as array spacing d , on the transmission beam are thoroughly discussed, which supplys theory of UPA trancducer choosing. An optimum UPA transducer is accordingly achieved.To improve automatic level of flaw qualitative analysis, pattern recognition method is introduced. Lifting Wavelet Transform (LWT) is proposed to extract features of flaw echoes in pipeline girth weld block. Combined with feature evaluation standard based on divisibility measure of distance, frequency-energy feature extraction form is applied. Wavelet Packet Transform (WPT) and LWT are used to extract energy feature and their results are compared. The testing result indicates that extraction time of LWT is nearly half of WPT.Fractal theory combined with LWT is put forward to extract the flaw signals'fractal dimension. Experimental results validate that divisibility measure of features with fractal is 6.28% more than that of features without fractal. This establishes a new path to extract flaw echoes'feature of pipeline welding.Simple Genetic Algorithm (SGA) is used to choose better features in extracted flaw echoes features. A best flaw features subset is determined, improving succeeding flaw identification efficiency.Artificial neural network (ANN) is adopted to implement automatic flaw identification of pipeline girth weld. Performance analysis and algorithm improvement are carried out on BP neural network, which has been used widely in practical application. The testing result is preferable. Moreover, RBF neural network with db4 LWT is built to identify the flaw waves in pipeline girth welds. The result shows that identification speed is greatly increased, but the accuracy is dramatically decreased. So RBF network is unfit for flaw identification of pipeline girth weld.Support vector machine (SVM) is a new technology in data mining field, and is brought forward for automatic flaw identification in ultrasonic NDT field. A SVM model with RBF kernel and SMO evolution algorithm is built. The improved BP network, RBF network and SVM are all tested on the same acquisition data of flaw echos. The experiment result suggests that SVM is superior to others on identifying speed and accuracy.The current UPA system generally is heavy and bulky. To overcome this shortcoming, a UPA flaw detection system for pipeline girth weld is developed. Experimental results validate its excellent flaw detection capability.