Theory and Application of Magnetic Flux Leakage Pipeline Detection

Теория и применение метода обнаружения дефектов трубопроводов по утечке магнитного потока
Rui Li, Yan Shi, Chao Zhang, Maolin Cai, Guanwei Jia
2015-12-10

MFL data measurement and processingleakage magnetic signal identificationmagnetic flux leakagenondestructive testingpipeline inspection
Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.
1
Advantages and disadvantages of different leakage-signal identification methods are analyzed, indicating trade-offs among approaches.
2
Expert systems applied to MFL detection are reviewed, and future developments in pipeline MFL detection are predicted.
3
Identification of leakage magnetic signals is a key step for quantitative MFL analysis, and multiple identification methods are discussed.
4
Magnetic flux leakage (MFL) is a widely used nondestructive pipeline inspection method that detects leakage magnetic fields from internal and external defects using magnetic sensors.
5
The paper presents the main principles, measurement techniques, and data processing approaches for MFL detection.

Magnetic flux leakage (MFL) pipeline inspection system (pipelines and their magnetic leakage fields caused by defects)

Identification, measurement, processing, and quantitative analysis of leakage magnetic signals for detection of internal and external pipeline defects using MFL, including comparison of identification methods and expert-system applications

Publication Details
Publication Date
2015-12-10
Journal
Publisher
ISSN
Access Type
Author Information
Authors
Rui Li
Yan Shi
Chao Zhang
Maolin Cai
Guanwei Jia
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%