Development of a convolutional neural network model to predict the size and location of corrosion defects on pipelines based on magnetic flux leakage signals

Abstract

This study employs the convolutional neural network (CNN) to predict the dimensions and locations of corrosion defects on steel pipelines based on magnetic flux leakage (MFL) signals. Extensive three-dimensional parametric finite element analyses are carried out to generate MFL signals corresponding to semi-ellipsoidal-shaped corrosion defects with different sizes and locations on a pipe model. The white noises characterized by different signal-to-noise ratios are considered in the analysis to represent the measurement errors in the real MFL inspection tool. The numerically generated MFL signals are used to train and validate the CNN model to predict the dimensions and locations of the corrosion defects. The results indicate that the developed CNN model achieves a high predictive accuracy. The study demonstrates the application of CNN model to improve the pipeline integrity management practice.