Research on the defect depth detection for pipeline steel with double defects using metal magnetic memory method

Abstract

Metal magnetic memory testing is an effective method to detect the stress state of ferromagnetic materials, and shows great application potential in the field of defect detection of oil and gas pipelines recently. In this paper, the correlation between the normal component of residual magnetic field (RMF) and the defect depth, as well as the applied loading were investigated by tensile tests. The X70 pipeline steels were processed into specimens with double defects of six types of depths and three types of spacings. Some magnetic parameters are defined to characterize the distortion of the RMF in the defect area. The effect of defect depth and applied loading on the magnetic parameters is discussed. The RMF signals measured on the smooth side of the specimens were compared with those on the defective side. Results show that the normal parameters, obtained from the front and back of the specimens, are capable of detecting the defect depth. The slope of the linear relationship between the two increases with the increase of defect spacing. The expressions of normal magnetic parameters were derived based on the Jiles model and magnetic dipole model. The simulation results of defective side and smooth side were compared, as well as the simulation results of different defect spacing. The experimental and numerical simulation results show that the defect depth of pipeline steel can be evaluated by the characteristic parameters of the normal RMF signal under a certain stress level.

Keywords

• Metal magnetic memory
• Defect depth
• Double defect
• Characteristic parameter
• Pipeline steels