Research On Fault Diagnosis Method Of Pipeline Leakage Based On Linear Fitting And Fuzzy Min-Max Neural Network

Oil pipeline operation in good condition is crucial for its fault diagnosis with the development of the pipeline transportation. Because of inevitable corrosion and man-made sabotage, the leakage of crude oil pipelines frequently happened. As a result, the research on fault diagnosis method of pipeline leakage takes position of theoretical and practical significance.But now, we always use the method based on only one theory in the field of pipelines’leakage detection. The negative pressure wave technology, which possesses straightforward principles, simple calculation, high sensitivity and outstanding immediacy, is widely used. However, this technology has little higher accuracy. Meanwhile, it is highly sensitive to the noise. What is more, it can not distinguish the right state between leakage and working condition rest on the pressure decline. Therefore, the thesis presents the method and the system based on the team working between the negative pressure wave and sound wave. The team working is built to raise accuracy of finding leakage point. Moreover, the angle change, which is inflicted by fitting lines, to some extent, can clear up the effects of noise and decrease error alarms. Fuzzy min-max neural network puts pressure features into use to represent the pattern classes, which include leakage pattern and working condition. The main research work is described as follows:Firstly, the method and the system based on team working between the negative pressure wave and sound wave are put forward after we master the basic principle of negative pressure wave. The thesis introduces the structures, locating algorithm and working procedure of the system so as to supply the object for the latter research.Secondly, based on the angle change inflicted by the fitting line, a new method is proposed to decrease the frequency of error alarms. The thesis introduces the detailed process of the angle change when pressure is rising and dropping. The rule of the angle change is obeyed as long as the angle change belongs to [90,270]. According to this rule, we can easily achieve success on the analysis of the pressure dropping if choosing parameters reasonably. Thirdly, three classical fuzzy min-max neural networks are presented, which are FMNN, GFMN and FMCN. Based on the analysis of the parameters of fuzzy Min-Max neural network, a new classification method rest on centroid for real data pattern is proposed. The availability of the new method is validated by simulation of IRIS dataset.Finally, four kinds of fuzzy min-max neural network introduced above are applied to the filed of pipelines’leakage detection. The angle change inflicted by fitting lines is utilized to obtain training features and then we do some research on accuracy and time consuming.According to the method proposed in the thesis, lots of simulation experiments are carried out by MATLAB, using pressure data obtained from real pipelines. They can ensure the experiment results with high applicability and creditability. In a word, the fault diagnosis method of pipeline leakage based on linear fitting and fuzzy min-max neural network is extremely viable and effective.