Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect

The damage of equipment manufactured with ferromagnetic materials in service can be effectively detected by Metal Magnetic Memory Testing (MMMT) technology, which has received extensive attention in various industry fields. The effect of stress or strain on Magnetic Flux Leakage (MFL) signals of ferromagnetic materials has been researched by many scholars for assessing stress concentration and fatigue damage. However, there is still a lack of research on the detection of stress corrosion damage of ferromagnetic materials by MMMT technology. In this paper, the electrochemical corrosion system was designed for corrosion experiments, and three different experiments were performed to study the effect of corrosion on MFL signals. The distribution of MFL signals on the surface of the specimen was investigated. The results indicated that both the normal component Hn and tangential component Ht of MFL signals presented different signal characteristics when the specimen was subjected to different working conditions. Finally, two characterization parameters, Sn and St, were defined to evaluate the corrosion degree of the specimen, and St is better. The direct dependence of corrosion depth on the parameter was developed and the average error rates between the predicted and measured values are 8.94% under the same working condition. Therefore, the expression can be used to evaluate the corrosion degree of the specimen quantitatively. The results are significant for detecting and assessing the corrosion defect of ferromagnetic materials.

Quantitative Identification of Pipeline Crack Based on BP Neural Network

In the paper, the Metal Magnetic Memory Testing signal of pipeline crack is extracted. The BP neural network is constructed and trained. The experiment shows that the BP neural network can effectively identify the crack parameters of oil and gas pipeline in quantitative.