Pipeline Irregular Defect Inversion for Magnetic Flux Leakage Detection System Based on Heterogeneous Multiclass Feature Fusion

Abstract

Defect inversion estimates the defect size quantitatively, which is a significant process in magnetic flux leakage (MFL) detection system. The actual measured defects are irregular due to the complex environment, which leads to signal deformation. Moreover, the sufficient and accurate features of irregular defects are hard to extract, so it is difficult to invert MFL irregular defects precisely. Therefore, an end-to-end irregular defect inversion method based on heterogeneous multi-class feature fusion (HMFF) is proposed to quantify pipeline defects for MFL detection system. The HMFF extracts the abstract features and external features of MFL heterogeneous signals, respectively, where the abstract features are extracted by two deep pre-trained models with the same structure, and the external features are extracted with guidance from expert experience. Then, two feature fusion stages are proposed to fuse the extracted multi-class features from MFL heterogeneous signals for the first time. Among them, multi-class feature fusion not only makes full use of deep abstract features, but also enhances the supervision of expert experience to improve the interpretability. In addition, the heterogeneous feature fusion enriches the extracted features, and reduces the impact of signal deformation. Finally, the fully connected layers with appropriate activation functions are applied to invert the defect sizes. The experiments on MFL measured signals are conducted to demonstrate the precision and robustness of the proposed method.