Magnetic Flux Leakage Testing does not require preprocessing of the test object, is easy to obtain detection signals, has a high degree of automation, and can detect different types of defects.
Application of Magnetic Flux Leakage Detection in Pipeline Damage Detection
Magnetic flux leakage detection technology is one of the most commonly used methods for oil and gas pipeline damage detection. It is a non-destructive detection technology that uses magnetic sensors to detect magnetic flux leakage signals and then detect pipeline defects. First, the principle and operation process of magnetic flux leakage detection are introduced, and then the important achievements of magnetic flux leakage detection technology in recent years in signal preprocessing, anomaly recognition and defect quantification are reviewed. Among them, the anomaly recognition and defect quantification methods based on deep learning are introduced in detail, and the shortcomings of these methods are analyzed. Finally, the future development of pipeline magnetic flux leakage detection is prospected.
Oil and natural gas are important energy and chemical materials for social production. Due to the advantages of strong durability, large storage capacity and easy operation of storage tanks, most oil and chemical industry materials are stored in storage tanks before secondary processing. In the storage tank, the fumes in the form of chemical evaporation will corrode the bottom of the tank to form defects, and the bottom plate of the tank will accelerate corrosion due to the storage of water-containing substances. According to survey statistics, 70% of tank damage is caused by floor corrosion. In addition, oil and natural gas are often transported through long-distance pipelines; and most pipelines are laid underground and exposed to high humidity or high ground pressure, which are prone to corrosion and deformation. If these damages are not discovered in time, they will lead to catastrophic accidents, such as casualties, property losses, production interruptions and environmental pollution. However, storage tanks, pipelines and such equipment have high requirements for damage detection technology, and need to ensure the diversity of detection locations, the timeliness of detection results, and prevent damage to equipment during the detection process. Therefore, non-destructive testing technology is widely used in damage detection of such equipment due to its advantages such as no damage to the detection area, simple operation and rapid response.
Nondestructive testing is an important part of equipment quality control. The use of this technology depends largely on the environment and material characteristics of the test object, such as the diameter, length, thickness, manufacturing method and potential discontinuity location of the material at the test site. The main purpose of nondestructive testing is to ensure the integrity of the test material without affecting the expected function of the equipment. In this complex application environment, many nondestructive testing technologies have been developed, such as radiographic testing, ultrasonic testing, magnetic particle testing and magnetic flux leakage testing (MFL). Among all nondestructive testing methods, magnetic flux leakage testing is one of the most commonly used methods in the oil and gas industry. It can obtain reliable, timely and analytical results. Since the 1960s, this technology has made great progress in the field of pipeline inspection.
Magnetic flux leakage testing does not require pre-processing of the test object, it is easy to obtain detection signals, has a high degree of automation, and can detect different types of defects such as pores, shrinkage holes, corrosion, etc. Although in the long-term research and application process, this technology has moved from qualitative detection of defects to quantitative detection, there are still some challenges, such as complex detection conditions, the need to process a large number of magnetic field signals, and the inability to accurately identify defects; the detection process is very sensitive to noise interference, and some smaller defect signals will be identified as noise signals; when the material contains impurities, it will significantly affect the detection signal; there is a lack of defect quantification theory, and it is impossible to invert accurate defect characteristics based on the detection signal, and it is heavily dependent on the operator's experience.
