Magnetic Flux Leakage Sensing: Current Practices and Mathematical Analysis

2007 ◽  
Author(s):  
Sushant M. Dutta ◽  
Fathi H. Ghorbel
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Defect Characterization ◽  
3 Dimensional ◽  
Hall Effect Sensor ◽  
Leakage Field ◽  
Lift Off ◽  
Flux Leakage ◽  
Insight Into ◽  
Current Practices

In this paper, we analyze magnetic flux leakage (MFL) sensing for the nondestructive evaluation (NDE) of ferromagnetic specimens. Understanding the processes involved in the creation of magnetic flux leakage fields and their measurement is critical to robotic inspection applications. In particular, robotic inspection of energy pipelines uses mobile robots to magnetize sections of the pipe and to measure the MFL signal to detect defects. We study current practices and motivate the need for improvements. To facilitate the analysis, we develop an analytical model to represent the 3-dimensional magnetic flux leakage field due to a surface-breaking defect in the specimen. The model is derived from first principles using the concept of dipole magnetic charge, and uses surface integrals to represent the MFL field as measured by a Hall-effect sensor. Simulations are performed which generate novel results, apart from reproducing experimental results from the literature. The mathematical tractability of the model is exploited to analyze its properties, such as scale–invariance, influence of lift-off, and the tangential MFL component. These properties give new insight into MFL sensing, interpretation, and defect characterization.

Experimental Study of the Influencing Factors of Elevator Cable Magnetic Flux Leakage Detection Signal

2013 ◽  
Vol 711 ◽  
pp. 327-332
Author(s):  
Yi Su ◽  
Zhen Zhang ◽  
Tao Zhang ◽  
Ming Li Yang ◽  
Mei Lin ◽  
...  
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Probe Design ◽  
Détection Signal ◽  
Detection Mechanism ◽  
Fracture Width ◽  
Lift Off ◽  
Elevator Cable ◽  
Flux Leakage ◽  
Detection Signal

The detection mechanism of Magnetic Flux Leakage (MFL) Method of elevator cable is proposed. Using Gauss-Mercury method to analyze the influence of different factors that lift-off value, fracture width, broken wires number and diameter and depth all that based on the collecting experimental system of MFL signals. The method can be used to optimize the detection probe design and detection signal processing.

Finite Element Analysis and Research on Corrosion Defect of Tank Floor

2016 ◽  
Vol 853 ◽  
pp. 514-518
Author(s):  
Zhi Jun Yang ◽  
De Shu Chen ◽  
Liang Chen ◽  
Yu Zhuo Liu ◽  
Ran An ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Finite Element ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Bottom Plate ◽  
Element Analysis ◽  
Corrosion Defect ◽  
Leakage Magnetic Field ◽  
Leakage Field ◽  
Flux Leakage

Storage tank is an essential vessel in petrochemical industry, and the corrosion of tank is an important reason for the safety hazard. The corrosion of tank bottom plate is more serious than the tank wall, and it is not easy to check and repair, when damaged to a certain extent it will cause the leakage of the media, then lead to waste of energy, environmental pollution, at the same time it will cause a major accident. Magnetic flux leakage testing is widely used in the field of tank floor inspection with the advantages of fast scanning speed, accurate results and so on. In this paper, the finite element simulation and analysis of the corrosion defect leakage magnetic field is used to obtain the data, and the characteristic of the leakage magnetic field is extracted. The effect of defect depth and width and shape on the magnetic flux leakage field is studied, and the distribution curve of the magnetic flux leakage field is obtained.

scholarly journals A Lift-Off-Tolerant Magnetic Flux Leakage Testing Method for Drill Pipes at Wellhead

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 201 ◽  
Author(s):  
Jianbo Wu ◽  
Hui Fang ◽  
Long Li ◽  
Jie Wang ◽  
Xiaoming Huang ◽  
...  
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Testing Method ◽  
Drill Pipes ◽  
Lift Off ◽  
Flux Leakage ◽  
Magnetic Flux Leakage Testing

scholarly journals A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage

2021 ◽  
Vol 11 (20) ◽  
pp. 9489
Author(s):  
Yinliang Jia ◽  
Shicheng Zhang ◽  
Ping Wang ◽  
Kailun Ji
Keyword(s):  
Magnetic Flux ◽  
Surface Defects ◽  
Rapid Development ◽  
Magnetic Flux Leakage ◽  
Detection Accuracy ◽  
Physical Experiments ◽  
Detection Technology ◽  
Element Simulation ◽  
Lift Off ◽  
Flux Leakage

With the rapid development of the world’s railways, rail is vital to ensure the safety of rail transit. This article focuses on the magnetic flux leakage (MFL) non-destructive detection technology of the surface defects in railhead. A Multi-sensors method is proposed. The main sensor and four auxiliary sensors are arranged in the detection direction. Firstly, the root mean square (RMS) of the x-component of the main sensor signal is calculated. In the data more significant than the threshold, the defects are determined by the relative values of the sensors signal. The optimal distances among these sensors are calculated to the size of a defect and the lift-off. From the finite element simulation and physical experiments, it is shown that this method can effectively suppress vibration interference and improve the detection accuracy of defects.

A novel magnetic flux leakage method based on the ferromagnetic lift-off layer with through groove

2021 ◽  
Vol 332 ◽  
pp. 113091
Author(s):  
Jian Tang ◽  
Rongbiao Wang ◽  
Bocheng Liu ◽  
Yihua Kang
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Lift Off ◽  
Flux Leakage

Numerical Simulation and Experimental Research on Crack Magnetic Flux Leakage Field

2011 ◽  
Author(s):  
Fujun Liu ◽  
Mulin Zheng ◽  
Shuai Kong ◽  
Zhangwei Ling ◽  
Yueqiang Qian
Keyword(s):  
Numerical Simulation ◽  
Magnetic Flux ◽  
Experimental Research ◽  
Inclination Angle ◽  
Three Dimensional ◽  
Magnetic Flux Leakage ◽  
Experimental Investigations ◽  
Leakage Field ◽  
Three Dimensional Models ◽  
Flux Leakage

Magnetic flux leakage (MFL) testing is widely used to inspect and characterize defects in pipelines, storage tanks and other structures. In this paper, based on the Maxwell Equations, numerical simulation and experimental research of crack magnetic flux leakage field were carried out. The three-dimensional models of cracks were established, the influence of the generalized crack parameters to the magnetostatic MFL field, including depth, width, inclination angle and crack spacing, was discussed. The relationship between defect parameters and MFL amplitude was obtained. The amplitude is significantly affected by the inclination angle. Therefore, single direction inspection may lead to undetected in practice. While the two cracks interval is less than 5 mm, the MFL fields would overlap. Furthermore, the experimental investigations were developed, and the results agree well with that of the simulation. The conclusions could provide valuable reference for inspection.

Optimising sensor pitch for magnetic flux leakage imaging systems

2021 ◽  
Vol 63 (7) ◽  
pp. 416-421
Author(s):  
R Murshudov ◽  
J M Watson ◽  
C W Liang ◽  
J Sexton ◽  
M Missous
Keyword(s):  
Magnetic Flux ◽  
Imaging System ◽  
Sensor Arrays ◽  
Magnetic Sensors ◽  
Magnetic Flux Leakage ◽  
Step Size ◽  
Optimal Spacing ◽  
Minimum Number ◽  
Lift Off ◽  
Flux Leakage

Sensor arrays can significantly increase the speed at which inspections and subsequent imaging of flaws is performed[1]. This work focuses on developing a software approach for optimising the spacing between quantum well Hall-effect (QWHE) magnetic sensors used for magnetic flux leakage (MFL) imaging, where this approach could be adapted for any non-destructive evaluation (NDE) technique in which imaging is obtained. A ground mild steel weld sample containing two surface-breaking flaws prepared by Sonaspection was scanned using an XYZ MFL imaging system developed at the University of Manchester[2,3,13,14]. The scan was taken with an autonomously controlled lift-off height of 0.75 mm, with an x-y measurement step of 100 μm and an applied magnetic field of 30 mT root mean square (RMS) at a frequency of 400 Hz. This data (ie magnetic image) was then processed to simulate different measurement step sizes, to determine any relationship between step size and flaw detectability (flaw signal to weld background response). This work effectively simulates different sensor pitches (separation between sensors) of integrated QWHE sensor arrays from 100 μm to 5 mm, with the goal of determining both the minimum number of sensors required in the array and the optimal spacing to maximise scan speeds and help determine optimum inspection parameters to develop the technology of low-power MFL imaging. This optimisation process could be applied to any NDE imaging system (electromagnetic or other) currently used, with results dependent on the inspection parameters.

scholarly journals An Analytical Model for Prediction of Magnetic Flux Leakage from Surface Defects in Ferromagnetic Tubes

2016 ◽  
Vol 16 (1) ◽  
pp. 8-13 ◽  
Author(s):  
V. Suresh ◽  
A. Abudhahir
Keyword(s):  
Magnetic Flux ◽  
Analytical Model ◽  
Surface Defects ◽  
Experimental Results ◽  
Magnetic Flux Leakage ◽  
Analytical Expression ◽  
Cylindrical Holes ◽  
Lift Off ◽  
Flux Leakage ◽  
Good Agreement

Abstract In this paper, an analytical model is proposed to predict magnetic flux leakage (MFL) signals from the surface defects in ferromagnetic tubes. The analytical expression consists of elliptic integrals of first kind based on the magnetic dipole model. The radial (Bz) component of leakage fields is computed from the cylindrical holes in ferromagnetic tubes. The effectiveness of the model has been studied by analyzing MFL signals as a function of the defect parameters and lift-off. The model predicted results are verified with experimental results and a good agreement is observed between the analytical and the experimental results. This analytical expression could be used for quick prediction of MFL signals and also input data for defect reconstructions in inverse MFL problem.

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