Guided ultrasonic detection system has the ability to inspect long range and not accessible pipelines. Especially, the T(0,1) mode guided wave was used widely at the detection, because the property of non-dispersive. For rapidly judge common features on pipe, this thesis makes an artificial neural network diagnosis system to separate and recognize the signals on pipeline. In the experimental setup, the torsional mode signal are excited by using an array of transducers distributed around the circumference of the 6-inch standard pipe, and the reflected signals contain flange, weld, elbow, and defect on elbow. These features are extracted and have been further processed to limit the size of the neural network; then, the feature signal classify as axisymmetric called black, non-axisymmetric called red, and dividing between the two called R/B ratio. The research also uses finite element method to simulate the weld by building up different kind of profile to analyze its amplitude and simulate the flange, elbow, and defect on the elbow. Because the reflection waves of simulation are too idealize to be the network data, the training data and validation data are collected from the experimental wave. In the recognition of artificial neural network, the signals were getting from two pipes of industry. One has bitumen on it, which makes signals attenuation. The other has a clear elbow and a notch on elbow. The two-class recognition method successfully separates flange and weld in low frequency; but in high frequency, the weld signal amplitude is close to flange signal, because the signals decay when guided waves pass to bitumen, and this makes the judge become error. Furthermore, the network recognizes defects on elbow, where the signals have 3 peaks and 2 peaks when the elbow has defect on it. The training result shows that the 3 peaks have better convergent than the 2 peaks in the network. Finally, the developed method can recognize those defects on the elbow when the reflection signals have 2 peaks, and when reflection signals have 3 peaks, it could not make a good judge because the network limit by sample data.

It is usually to see a large number of pipelines separating around the refineries, chemical and petro-chemical plants. The corrosion and erosion defects are unavoidable to occur in transporting pipe line. Especially, the maintain stuff usually find out breakage pipe or leaking liquid at elbowing pipe line because of the corrosion and erosion defects. So it is essential to examine these pipelines with an efficient method. The use of guided waves method is very attractive to solve this problem since guided wave could be excited at one circle on the pipeline and propagate over considerable distance. To choose guided wave torsion mode T (0, 1) as excitation mode because its group velocity doesn¡¦t change with frequencies. And the research analyzes the mode conversion that occurred when T (0, 1) mode propagated after the elbow pipe. The research also discusses the signal difference in different depth, circumferential distribution and axial length defects on the elbow pipe. The erosion defect usually occurs in the elbow pipe line and it would change with fluid velocity, causticity of fluid and flow direction. Therefore, the research designs the defects according to the character of erosion defect by finite element method software and simulates T (0, 1) mode propagating in the pipe line. Then this research extracts and analyzes the reflection signals from defects. In this guided wave experiment, the research manufactures the defect on elbow pipe. Because the erosion defect could be usually found at outer side of elbow pipe, artificial defect would be set there. And the elbow pipe is manufactured with different depth, circumferential distribution and axial length defect. The research would discuss the relationship between change of defect and reflection signal. By elbow pipe defect signals of simulation and experiment consequence, the different depth, circumferential distribution and axial length defect signals could be still distinguished. The signals with different axial length defect that received from straight pipe and elbow pipe are similar and are affected by signal constructive and destructive interference. So the research could get maximum and minimum defect signal amplitudes from one-fourth wavelength axial defect and half wavelength axial defect. Therefore, the axial length defect of elbow pipe could be estimated from defect signals and this consequence could help judge the level of damaged elbow pipe. T (0, 1) mode has better sensitivity to outside of the pipe than inside of the pipe. So the bigger signal amplitude could be received from the notch at outside of the pipe. In the process of wave propagation simulation, there are overlapping waveforms and mode conversions occur at elbow pipe. This situation causes the defect signals were amplified at elbow pipe. In practical detection, the misjudgments of amplified defect signals should be attended to.

Using ultrasonic guided waves can achieve long range inspection along the pipeline rapidly. The presence of defect or other features on the pipe were identified by analyzing the reflected echoes as well as mode conversion phenomena. However, it is difficult for guided wave to find a minor corrosion, such as pitting. Therefore, a study of the reflection of torsional T(0,1) mode from pits on the pipe has been carried out and an advanced signal processing method wavelet transform is adopted to process the reflected echoes in this study. In order to understand that characteristic of the reflected echoes of pits, the propagation of guided wave T(0,1) through pits was simulated by the finite element method. The frequency response of the signal reflected from the pits with different sizes was discussed both by finite element method and experimental method. Then, we discuss two types of pitting including regular- distributed pitting and the random-distributed pitting. We not only discuss the relation between the axial length of regular pitting and wave length of the T(0,1) mode, but also the reflected singal of four random pittings. The experiments were performed on 3 inch carbon steel pipe for measuring the reflected signals from different pittings with different frequencies. The results of the simulation, indicate that the wave was easily scattered by pitting because the shape of geometry. It is the reason of reducing the amplitude of reflected signals. To receive a dominate signal reflected from pitting, the excitation with higher frequency was choosen within the frequency range of interest. The experimental results indicate that the signals would be too weak to be detected by guided waves when the estimated cross sectional loss of the pitting is less than 2 percent. However, the results after wavelet transform showed the feasibility of improving the abilities of detecting minor pitting. In the case of regular pitting, the maximu value of the reflected signal appeared when the axial length of the pitting equals to the 66 % of the wavelength. It is because the constructive interference. The mode conversion phenomena is another behavior of the reflected signal cased by the non-axissymetric geometry of the pitting. As for the random pitting, The reflected echo shows different behavior with the regular pitting. The amplitude of the signal is bigger with lower frequency we use. The different level of random pitting on the pipe were also identified successfully by wavelet transform. Understanding the phenomena of interaction between the guided wave and the pitting is helpful to the guided wave inspection.