Defect Size Inversion for Distorted Pipeline MFL Signals Caused by Speed Effect via Multiple Information-Guided Collaborative Network

Magnetic flux leakage (MFL) detection has been widely used in the pipeline integrity evaluation. However, the MFL signals will be distorted due to the speed effect, which reduces the accuracy of the defect size inversion severely. To solve above problems, a multiple information-guided collaborative network (MIG-CN) is proposed to invert defect sizes accurately for the distorted pipeline MFL signals caused by the speed effect. First, a multiview feature fusion method is proposed, which enables MIG-CN to predict the defect sizes of specific types accurately by fusing the features of specific views, where the mechanism features are injected into the network to fully exploit the physical information of the MFL signals, improving the inversion accuracy. Second, a physics-informed constrained network is proposed, in which a speed magnetic dipole model is developed to impose constraints from real physical laws with the speed effect, thus enhancing the physical plausibility of the inversion results. Third, a dynamic-static collaborative decision strategy is proposed, in which a two-point constrained quantile probability modelling is proposed to model the inversion results of the undistorted MFL signals and obtain the prior probability distribution. The accuracy and robustness of the inversion results at different speeds are improved by minimizing the difference between the probability distribution of the inversion results of the distorted MFL signals and those of the undistorted. The above three components are jointly trained and optimized to improve the inversion accuracy under the influence of speed effect. Finally, the experiments on real pipeline networks verifies the effectiveness of MIG-CN.