Research On Fault Diagnose Method Of Pipeline Leakage Based On Hopfield Neural Network

Because of the special advantage pipeline is playing in transiting liquid and gas, pipeline is becoming more and more irreplaceable in the field of oil transportation. But as pipeline de-velops, aging, erosion and man-made damage of the pipeline cause leakage frequently, which make large economic loss, big environmental pollution and serious security problems. To find pipeline leakage quickly and accurately has great economic and social significance for pro-tecting both environment and national property.Based on the theory of fault-diagnose, a method is proposed to detect the oil pipeline leakage by the theory of Hopfield neural network. As commonly used pipeline leak detection methods detect only the point of descending edge of the pressure, error alarms often occur. In this thesis, a Hopfield network is built to detect leakage based on not only a leak-point but also a pressure chart of leakage. The leak detection process is divided into two stages:the training stage and the detecting stage. At the training stage, the network model is established by taking the target vector which is obtained from the pressure series of leakage as input. At the detecting stage, a pressure series is transformed and put into the neural network to operate. When the network stands in balance, the output vector is compared with the vector in data-base to judge it is leakage or not. The main research work is described as follow:Firstly, based on the theory of wavelet, a method is proposed to denoise the signal, which provides a sound base for the leak detection with Hopfield neural network.Secondly, a method is proposed to change the denoised pressure series into vector form. And the method to detect leakage with Hopfield neural network is also given.Finally, based on the research above, two methods to detect leakage online are given. Wavelet theory is also imported to find the accurate location of leakage in the second method.According to the method proposed in this thesis, lots of simulation experiments are car-ried out with the software of MATLAB, using pressure data obtained from real pipeline to ensure the experiment results with high creditability and applicability. Simulation results prove that the pipeline leakage can be detected accurately by the methods proposed in the the-sis, and the method can also reduce error alarms significantly, which adequately testify the powerful processing ability of this method.