A novel method for defects marking and classifying in MFL inspection of pipeline

Abstract

Magnetic Flux Leakage (MFL) testing is the most widely used non-destructive technique for the inner inspection of oil and gas pipelines. Accurate quantification of defects is a long-standing difficulty in the field of pipeline leak detection. Scientific marking and classification of defects is an important prerequisite for accurate quantification. A novel method of marking and classifying defects with MFL signals is proposed, which is aiming at the problem that it is difficult to mark and classify defects accurately due to the tremendous amount of oil and gas pipeline leakage magnetic detection data. An improved CLIQUE algorithm is used to mark the defect areas of segmented pipelines to estimate the number and location of defects. Then the 3D MFL characteristic signals of the marked areas are studied and extracted. The SSA_BP neural network is trained to classify the defects. The effectiveness and accuracy of the proposed method are tested and verified by using finite element simulation defects and actual defects. The results show that the method is more efficient in marking defects and more detailed in marking areas.