A Simplified Lift-Off Correction for Three Components of the Magnetic Flux Leakage Signal for Defect Detection

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.

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Enhancement method of magnetic flux leakage signals for rail track surface defect detection

IET Science Measurement & Technology ◽

10.1049/iet-smt.2018.5651 ◽

2020 ◽

Vol 14 (6) ◽

pp. 711-717

Author(s):

Yinliang Jia ◽

Kangwu Liang ◽

Ping Wang ◽

Kailun Ji ◽

Peng Xu

Keyword(s):

Magnetic Flux ◽

Defect Detection ◽

Surface Defect ◽

Magnetic Flux Leakage ◽

Rail Track ◽

Surface Defect Detection ◽

Enhancement Method ◽

Track Surface ◽

Flux Leakage

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

Sensors ◽

10.3390/s17010201 ◽

2017 ◽

Vol 17 (12) ◽

pp. 201 ◽

Cited By ~ 12

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

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Certain studies on thresholding based defect detection algorithms for Magnetic Flux Leakage images

2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN) ◽

10.1109/ice-ccn.2013.6528552 ◽

2013 ◽

Author(s):

S. Janakiraman ◽

J. Daniel ◽

A. Abudhahir

Keyword(s):

Magnetic Flux ◽

Defect Detection ◽

Magnetic Flux Leakage ◽

Detection Algorithms ◽

Flux Leakage

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DCT AND MLP IN THE APPLICATION OF MAGNETIC FLUX LEAKAGE DEFECT DETECTION

Acta Tecnología ◽

10.22306/atec.v6i4.96 ◽

2020 ◽

Vol 6 (4) ◽

pp. 119-122

Author(s):

Saeedreza Ehteram

Keyword(s):

Magnetic Flux ◽

Defect Detection ◽

Magnetic Flux Leakage ◽

Multilayer Perceptrons ◽

Mathematical Function ◽

Accuracy Rate ◽

Destructive Testing ◽

Real Component ◽

Surface Emission ◽

Flux Leakage

Non-Destructive Testing (NDT) is known as a harmless technique for industrial pipeline cyclic inspection. This way tries to find out defected parts of a device used in industry with a test by means of non itself destroying. Many ways are known and employed in NDT procedure. MFL or magnetic Flux Leakage is one of well-known and so efficient ones is widely used to find out defects in metal surface. Emission of magnetic field into device surface and recording reflected emission lead to complete a database of defect and no defect for an especial task. Then mathematical equations could help to provide normalization and classification ahead. Defect and non-defect detection are an essential and cost loss technique for analyse data from cyclic inspections. For this purpose a combination of neural networks is designed and trained in the best performance and with optimum accuracy rate. In this model Classification is done via Multilayer Perceptrons (MLP). Two level of classification is applied. First defect categorization and then defect or non-defect detection. In this paper a mathematical function named DCT or Discrete Cosine Transform is applied in pure database for data compression. This function provides a view on database in real component of frequency domain. By composing DCT function with a neural network group, this algorithm provides 97.3 percent accuracy rate in defect detection of MFL signals.

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

Applied Sciences ◽

10.3390/app11209489 ◽

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.

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Tsallis Entropy Segmentation and Shape Feature-based Classification of Defects in the Simulated Magnetic Flux Leakage Images of Steam Generator Tubes

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420540026 ◽

2019 ◽

Vol 34 (01) ◽

pp. 2054002

Author(s):

Jackson Daniel ◽

A. Abudhahir ◽

J. Janet Paulin

Keyword(s):

Magnetic Flux ◽

Steam Generator ◽

Defect Detection ◽

Performance Metrics ◽

Tsallis Entropy ◽

Magnetic Flux Leakage ◽

Equivalent Diameter ◽

Detection Accuracy ◽

Back Propagation Algorithm ◽

Flux Leakage

Early detection of water or steam leaks into sodium in the steam generator units of nuclear reactors is an important requirement from safety and economic considerations. Automated defect detection and classification algorithm for categorizing the defects in the steam generator tube (SGT) of nuclear power plants using magnetic flux leakage (MFL) technique has been developed. MFL detection is one of the most prevalent methods of pipeline inspection. Comsol 4.3a, a multiphysics modeling software has been used to obtain the simulated MFL defect images. Different thresholding methods are applied to segment the defect images. Performance metrics have been computed to identify the better segmentation technique. Shape-based feature sets such as area, perimeter, equivalent diameter, roundness, bounding box, circularity ratio and eccentricity for defect have been extracted as features for defect detection and classification. A feed forward neural network has been constructed and trained using a back-propagation algorithm. The shape features extracted from Tsallis entropy-based segmented MFL images have been used as inputs for training and recognizing shapes. The proposed method with Tsallis entropy segmentation and shape-based feature set has yielded the promising results with detection accuracy of 100% and average classification accuracy of 96.11%.

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ANSYS Simulation Analysis of the Tri-Axial Pipeline Magnetic Flux Leakage In-Line Inspection

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.2086 ◽

2012 ◽

Vol 490-495 ◽

pp. 2086-2090

Author(s):

Li Jian Yang ◽

Yan Xiu Su ◽

Song Wei Gao

Keyword(s):

Magnetic Field ◽

Finite Element Method ◽

Magnetic Flux ◽

Simulation Analysis ◽

Magnetic Flux Leakage ◽

Ansys Simulation ◽

Leakage Magnetic Field ◽

Simulation Results ◽

Flux Leakage ◽

Three Components

In order to improve the detect performance of the traditional pipeline magnetic flux leakage(MFL) in-line inspection tools, the method of tri-axial pipeline MFL in-line inspection is advanced. By the three leakage magnetic field components: axial, radial and circumferential, the pipeline defects are recognized. Applying finite element method to build the simulation model of pipeline MFL in-line inspection, then proceeding 3-D simulation analysis. The three components of leakage magnetic field (axial, radial and circumferential) can be obtained by using ANSYS 3-D simulation, and the pipeline defects’ existence as well as the change of the defect’s size can be estimated by the three components’ graph. The simulation results indicate: By the ANSYS 3-D simulation, it proves that the tri-axial pipeline MFL in-line inspection can be achieved and the inspection can be used to improve the level of traditional pipeline MFL in-line inspection.

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