Optimum Design Of Near-field Eddy Current Detection Module For A Kind Of Magnetic Thickness Gauge

The magnetic thickness gauge is an instrument for evaluating the thickness of metal pipes based on the theory of electromagnetic eddy current testing technology.The purpose of the design is to detect the thickness of the pipeline and to evaluate the damage information of the pipeline.Based on the theory of near-field eddy current testing,the near-field eddy current testing module of magnetic thickness gauge is designed to detect the inner diameter and permeability of metal pipeline.The main work of this thesis is as follows:According to the basic principle of near-field eddy current testing,a simplified approximation is made,and the mapping relationship between the phase difference between the received signal and the excitation signal affected by the pipeline and the pipeline parameters is derived.With the help of finite element analysis software Comsol to simulate the pipeline environment of eddy current testing,the simulation model of pipeline eddy current testing is established.By changing pipeline parameters,the phase difference information between the received signal and the excitation signal under different pipeline parameters is obtained,and then the inversion database from pipeline parameters to phase characteristics is established.Furthermore,the data of phase difference varying with temperature in air are obtained by further simulation,and the relationship between phase difference and temperature in air is obtained by curve fitting approximation.In view of the weak and multi-frequency mixing characteristics of near-field eddy current detection signal,the calculation methods of phase difference between received signal and excitation signal are discussed,including digital correlation method,phase sensitive detection method and FFT spectrum analysis method,and the principles,advantages and disadvantages of each calculation method are compared.In addition,according to the requirement of near-field circuit system,the excitation signal generation circuit and reception signal processing circuit are designed.The frame structure of ADC+FPGA+DSP is used to collect the received signal and reference signal with high precision,and the phase difference between them is calculated accurately by FFT spectrum analysis method.The phase difference data is transmitted to the main control circuit board,and uploaded to the upper computer by the main control circuit board.According to the characteristics of data in inversion database,BP neural network and LSSVM algorithm are used to train and learn the mapping relationship between phase features and pipeline parameters in inversion database.Aiming at the problems existing in the training process of the algorithm,the chicken swarm optimization algorithm is introduced,and some defects in the chicken swarm optimization algorithm are improved.The improved chicken swarm optimization algorithm is used to optimize the BP neural network and LSSVM algorithm,and the performance of the algorithm model is analyzed and compared from various aspects,from which the relatively optimal pipeline parameter inversion algorithm is selected as the final one.On the basis of the design of circuit system and pipeline parameter inversion algorithm,the magnetic thickness gauge is used to test the experimental pipeline and verify that the performance index of the near-field eddy current detection module meets the requirements.The reliability,accuracy and engineering application ability of the instrument are verified by further testing the downhole oil casing with the instrument.