Quantitative method of pipeline magnetic leakage internal signal detection on the basis of an improved neural network.

In response to the problem of poor quantification ability and unclear feature correlation of pipeline defect magnetic flux leakage signals, a neural network is proposed to establish the relationship between the characteristic quantities of pipeline defect magnetic flux leakage signals and defect sizes. The characteristic quantities of pipeline defect magnetic flux leakage signals that measure the length, width and depth of pipeline defects are determined by combining the characteristic quantities of the radial and axial components of defect magnetic flux leakage signals in actual pipelines. A database of magnetic leakage signal characteristic quantities for pipeline defects is established by extracting and organizing the characteristic quantities of magnetic leakage signals. Moreover, a particle swarm optimization (PSO)-radial basis function (RBF) neural network model that combines the PSO algorithm and the RBF neural network to quantify pipeline defects is designed. Results show that the average quantification error of the PSO-RBF network model reached 21.08%, representing an improvement of 12.94% compared with that of the traditional RBF network model. The Pearson correlation analysis shows that these feature quantities are significantly positively correlated with the defect size, which provides a reliable feature basis for quantitative modeling.It meets the requirements of practical engineering applications for pipeline defect quantification and has a good application prospect in pipeline magnetic leakage internal detection technology.