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- Title
MFL detection of adjacent pipeline defects: a finite element simulation of signal characteristics.
- Authors
Mo He; Zhiyong Zhou; Lin Qin; Hao Yong; Chao Chen
- Abstract
Magnetic flux leakage (MFL) is one of the most commonly used non-destructive testing technologies for defect detection of oil and gas pipelines. Analysing the MFLsignals of different defects and thus identifying the types and sizes of pipeline defects are the key and difficult points, obtaining wide attention in both academic and engineering domains. Most of the past research has focused on the MFL signals of single defects, neglecting the interference caused by adjacent defects, possibly leading to errors. As a result, this study develops a finite element method (FEM) model based on Maxwell theory for the MFL signal of adjacent defects and analyses the signal characteristics, considering both inner and outer defects The interference distances caused by inner and outer defects are analysed and the shape and size of the defects are also considered to identify defects in multiple adjacent defects. The model results show that the interference caused by adjacent defects manifests the superposition of the leakage magnetic field in axial and radial components. The interference weakens with increasing distance between adjacent defects. To quantify the interference caused by different defects, a concept of interference distance' is developed using the change rate of the peak value of MFL signals. The influence of different factors on the interference distance is explored by analysing the MFL signal under different factors. Therefore, this study can support the identification of adjacent defects on steel pipelines using MFLtechnology, reducing the errors caused by adjacent defects.
- Subjects
MAGNETIC flux leakage; NONDESTRUCTIVE testing; FINITE element method; PETROLEUM pipelines; SIGNAL theory
- Publication
Insight: Non-Destructive Testing & Condition Monitoring, 2024, Vol 66, Issue 6, p353
- ISSN
1354-2575
- Publication type
Article
- DOI
10.1784/insi.2024.66.6.353