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<p>Magnetic Flux Leakage (MFL) system has become one of the primary non-destructive testing (NDT) methods used in the prediction, control and maintenance of pipelines. MFL allows real-time evaluation of structural integrity by putting a magnetic field into the material of the pipeline and recording the changes that occur due to corrosion, cracks, and metal loss. The review is a systematic analysis of some of the studies that were published during 2019–2024 as a method to assess the solution of using MFL to improve predictive maintenance models and to increase safety and operational reliability of industries. Recent progress in predictive control strategies, enhancement of MFL sensor design, defect detection and classification schemes, and the combination of MFL with artificial intelligence (AI) and machine learning (ML) in data-based diagnostics are discussed in the review. Moreover, it addresses the development of MFL technology in the contemporary pipeline inspection and introduces comparative information on its functionality, constraints, and capability in a hybrid system of predictive maintenance. Through the synthesis of these research patterns, the research paper creates an overall view of the role of MFL in smart, active pipeline management and its possibilities in the future in the context of infrastructure monitoring in Industry 4.0.</p>

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Using the magnetic flux leakage system as a predictive controlling method for pipelines

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Edem Ayivi Mawugbe,
  • Yuxing Li,
  • Christopher Nkansah,
  • Maxwell Bawuah,
  • Augustus Boadi

摘要

Magnetic Flux Leakage (MFL) system has become one of the primary non-destructive testing (NDT) methods used in the prediction, control and maintenance of pipelines. MFL allows real-time evaluation of structural integrity by putting a magnetic field into the material of the pipeline and recording the changes that occur due to corrosion, cracks, and metal loss. The review is a systematic analysis of some of the studies that were published during 2019–2024 as a method to assess the solution of using MFL to improve predictive maintenance models and to increase safety and operational reliability of industries. Recent progress in predictive control strategies, enhancement of MFL sensor design, defect detection and classification schemes, and the combination of MFL with artificial intelligence (AI) and machine learning (ML) in data-based diagnostics are discussed in the review. Moreover, it addresses the development of MFL technology in the contemporary pipeline inspection and introduces comparative information on its functionality, constraints, and capability in a hybrid system of predictive maintenance. Through the synthesis of these research patterns, the research paper creates an overall view of the role of MFL in smart, active pipeline management and its possibilities in the future in the context of infrastructure monitoring in Industry 4.0.