A Pipeline Leak Alarm Method Base On Arttificial Neural Network

In recent years,with the rapid development of oil industry,as an important transportation way,pipeline transportation is booming rapidly.But because of the aging,corrosion and duing to the economic interests driving the artificial theft and other reasons,resulting in frequent pipeline leak,thus causing significant economic losses,but also pollute the environment and improve the security risks.So how to quickly and accurately detect pipeline leaks,for the protection of the natural environment and the state property has great significance.So,how to reduce the fale alarm rate and leakage alarm rate becomes the focus of the paper.Subject to pipeline leaks in diagnosing the problem,based on the use of neural network theory and method for pipeline leak detection is a deeper study.In light of the problems of high false rates,high leakage rates,long alarm delay in traditional pipeline leakage recognition technology,the paper proposed a type of hierarchical leak detection method.According to the signal characteristics at different stages of signal processing,different singnal processing methods were taken.Firstly,preprocessing the original signal.To achieve frequency decomposition of the signal mixed with noise of the oil pipeline,the paper applied wavelet transform technology,thus separating the useful signal.However,due to the complex and changing environment along the pipeline,Using the wavelet transform simply can not identify the leak effectivelyThe paper presented a method that BP neural network based on wavelet technology,to recognize and alarm the leak status.Furthermore,the detect system used the wavelate method to extract the characteristic parameters of the three types of pipeline status signal collecting from the field,for the subsequent recognition and classification of the pipeline state.Secondly,in the signal identification phase,a model based on BP Neural Network is constructed,and the characteristic parameters obtained from the preprocessing phase are sent as the input of the BP Neural Network to correct the network and determine the network parameters.In the final judgement stage,the characteristic value collected from the real project were sent into the ner to recognize and classify.Finally,in view of the BP neural network exists convergence rate slow and easy to fall into partially smallest and so on questions,the paper proposed the genetic algorithm to impove the BP neural network,establishing a diagnostic model base on the combination of Genetic Algorithm and BP neural network.In the model,with the weight of BP neural network encoded,the population is evoluted to the optimal value,to optimized the structure and parameters of the BP network with the GA.Experimental results illustrate that Genetic Algorithm can overcome the problem of the BP Neural Network which is easy to fall into partially smallest effectively.At the same time,it can speed up the convergence of the network.The hierarchical leak detection method is able to improve the accuracy of leakage alarm,reduce the false alarm rate as well as undetected rate,and improve the reliability of the leak detection.And it is valuable in the academic and engineering application.