Research On Oil Pipeline Leak Diagnosis Based On Analysis Of Chaotic Time Series

Oil pipelines often leak because of aging, steal etc, so it is an important problem to detect the leak points on oil transmission pipelines. Many methods based on negative press wave, sound wave and optical fiber are used to find leak of oil pipelines, of which negative press wave is the common method in china. By far fluctuation of oil pipeline press time series (OPPTS) is assumed stochastic or white noise, which hinders the improvement of fault diagnosis system that designed based on pressure fluctuation. In this dissertation, the internal dynamic of pressure fluctuation in oil pipelines is reseached, based on which several new leak-detection motheds is proposed, the main work is as follow:1 RC-filter, mean-filter and wavelet-filter are used in turn to minish the effect of noise before studying internal dynamic of OPPTS. Filting of disperse wavelet, lifting wavelet and undecimated wavelet is discussed and is simulated separately, the result is the undecimated wavelet is the most fitting method.2 An improved method of computing the Lyapunov-spectrum based on Darbyshire-Broomhead’s arithmetic is given, in which the delay is computed based on mutual-information function, the optional embedding is obtained based on false nearest neighbors (FNN). The improved method can get the number of Lyapunov exponents and can evaluate Lyapunov-spectrum of OPPTS including noise. This method can avoid the step of removing spurious exponents.3 To improve the performance of leak detection system based on OPPTS, the possibility of existing chaotic characters is validated using the method of nonlinear analysis. Experimental data sets of OPPTS are studied on which phase spaces are reconstructed, fractal dimensions and Lyapunov exponents are computed, the stationarity and nonlinearity are validated. By the analysis of the results, the rigorous chaotic characters in OPPTS are found, that is a theoretical basis for the correlative investigation based on pressure time series of oil pipelines.4 An on-line fault-detection method of BP and RBF based on chaos for OPPTS is proposed. After reconstructing phase-space by OPPTS and using vectors of reconstruction as the input of neutral network model, this proposed method can real-time modify the parameters of model by training the model on-line. Then the default and even little fault can be diagnosed by the error between real-time data and the predicted value of neural network model. Experiential datasets of OPPTS are simulated by the proposed method.5 Another on-line fault-detection method of retina neural network based on chaos for OPPTS is proposed and the model of retina neural network is given also. This mothod has the excellences of needing little data and faster speed. Then the comparation and the situation of the methods based on BP, RBF and retina neural network is analysed.6 A leak diagnosis expert system of oil pipeline is desgined, which classes the fault data of OPPTS by fuzzy reasoning and finds fault point of OPPTS by on-line fault-detection method. The expert system has the better result than the old one by simulation,.