Research On The Application Of BP Neural Network In Pipeline MFL Digital Signal

The periodic inspection of pipeline is one of the most important means to assure the safe operation of the gas pipeline network. In the past,the pipeline defect inspection was done based on the peculiarity of MFL signal mostly by the technicians who was experience in it,so the result can be artificially impacted by the technician greatly,and it also will take a long time to do it.Furthermore,the technique in pipeline inspection only can estimate the degree of pipeline defect before,but can not describe the profile and the figure of defect at detail,and the interpret of data from the PIG was keeped a secret by the corporations over the world.For above,we need a new approach applied in processing pipeline MFL signal to solve the problerms.However,the technique of neural network has the strong point and character to done the problerms,so it was employed.What we has done in this paper is that BP Neural Network is designed to learn and train the data of pipeline magnetic flux leakage (MFL) signal so that we can get to know the character of the MFL signal ,then we are able to describe a method to map the profile of the pipeline defects.Therefore,the purpose of intelligent inspection in pipeline is done.Firstly,the basis theory of MFL detecting is introduced in this paper,then the character of the pipeline MFL signal is achieved by using the finite element analysis software (ANSYS),which can manage to gain the data of simulated pipeline MFL signal.Secondly,the detail discussions of artificial neural networks are presented,and the algorithmic model of BP Neural Network also is deduced,then according to the need of practice ,we do some modifying in the basic algorithm of BP Neural Network .At last we select to combine the two optimizations of conjugate gradient algorithmic and LM algorithmic to study and learn the MFL digital signal.Next,we used the modified BP algorithmic to study the simulated pipeline MFL signal and the signal from field,and we also compared the result from the network to the sampling.It isshowed that the effect of error is good.From above we can get to know that the application of BP Neural Network in processin pipeline MFL signal is possible.Finally,a hardware technique in implementing the application of BP Neural Network in processing pipeline MFL signal is put forward.By particularly discussin the specialty of the Very High Speed Integrated Circuit Hardware Description Language (VHDL) and do some research on the development of the Programmable Logic Device ,we can deeply assure that the great outlook of VHDL in implementing the application of BP Neural Network is coming true.