Multisensor Fusion for Magnetic Flux Leakage Defect Characterization Under Information Incompletion

Abstract

Magnetic flux leakage (MFL) inspection robots are widely used in the pipeline inspection industry to obtain MFL information from multisensor sources. Multisensor fusion is required for precise defect characterization. In this article, a multisensor fusion framework is presented to fuse and enhance MFL information for defect characterization. First, a space-vector-based information fusion method is proposed for better reflecting the spatial distribution of real magnetic field, where the direction and amplitude information of magnetic field are considered simultaneously. Second, considering the information incompletion caused by unstable inspection environment and abnormal device, the improved conditional generative adversarial nets are designed to enhance MFL information. These networks can effectively reconstruct multisensor information by refining the generator loss function, so that the enhanced MFL information can be better applied for defect characterization. Experimental results are compared with state of the art in detail, which highlight the superiority of the proposed multisensor fusion method in MFL inspection.