Stress effects and magnetic flux leakage induced by defects in pipelines

2000 ◽  
Author(s):  
L. Clapham
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Stress Effects ◽  
Flux Leakage

scholarly journals Detection of Mechanical Damage Using the Magnetic Flux Leakage Technique

2004 ◽  
Author(s):  
Lynann Clapham ◽  
Vijay Babbar ◽  
James Byrne
Keyword(s):  
Magnetic Flux ◽  
Experimental Studies ◽  
Mechanical Damage ◽  
Central Peak ◽  
Magnetic Flux Leakage ◽  
Element Analysis ◽  
Stress Effects ◽  
Stress Relieving ◽  
Stress Dependent ◽  
Flux Leakage

Since magnetism is strongly stress dependent, Magnetic Flux Leakage (MFL) inspection tools have the potential to locate and characterize mechanical damage in pipelines. However, MFL application to mechanical damage detection faces hurdles which make signal interpretation problematic: 1) the MFL signal is a superposition of geometrical and stress effects, 2) the stress distribution around a mechanically damaged region is very complex, consisting of plastic deformation and residual (elastic) stresses, 3) the effect of stress on magnetic behaviour is not well understood. This paper summarizes recent results of experimental and modeling studies of MFL signals resulting from mechanical damage. In experimental studies, mechanical damage was simulated using a tool and die press to produce dents of varying depths in plate samples. Radial component MFL measurements were made before and after selective stress-relieving heat treatments. These annealing treatments enabled the stress and geometry components of the MFL signal to be separated. Geometry and stress effects generate separate MFL peaks — the geometry effects lead to central peak regions while the stress effects produce ‘shoulder’ peaks. In general the geometry peaks tend to scale with depth, while the shoulder peaks remain approximately constant. This implies that deep dents will display a ‘geometry’ signature while shallow or rerounded dents will have a stress signature. Finally, the influence of other parameters such as flux density and topside/bottomside inspection was also quantified. In the finite element analysis work, stress was incorporated by modifying the magnetic permeability in the residual stress regions of the modeled dent. Both stress and geometry contributions to the MFL signal were examined separately. Despite using a number of simplifying assumptions, the modeled results matched the experimental results very closely, and were used to aid in interpretation of the MFL signals.

scholarly journals Detection of Mechanical Damage Using the Magnetic Flux Leakage Technique

2002 ◽  
Author(s):  
L. Clapham ◽  
Vijay Babbar ◽  
Thana Rahim ◽  
David Atherton
Keyword(s):  
Residual Stress ◽  
Plastic Deformation ◽  
Magnetic Flux ◽  
Mechanical Damage ◽  
Magnetic Behaviour ◽  
Magnetic Flux Leakage ◽  
Stress Effects ◽  
Stress Relieving ◽  
Series Of Experiments ◽  
Flux Leakage

Since magnetism is strongly stress dependent, Magnetic Flux Leakage (MFL) inspection tools have the potential to locate and characterize mechanical damage in pipelines. However, MFL application to mechanical damage detection faces major hurdles, which make signal interpretation problematic: 1) the MFL signal will be a superposition of geometrical and stress effects, 2) the stress distribution around a mechanically damaged region is very complex, consisting of plastic deformation and residual (elastic) stresses, 3) the effect of stress on magnetic behaviour is not well understood. This paper summarizes a number of our studies concerned with mechanical damage and the effects of elastic and plastic deformation on MFL signals. The first series of experiments was conducted using uniaxial loading into the plastic deformation regime. Magnetic measurements made in situ with this uniaxial deformation showed that magnetic behaviour is far more sensitive to elastic, compared to plastic, deformation. Unloading the samples resulted in a combination of plastic deformation and residual stress. Subsequent ‘staged’ stress relieving heat treatments enabled us to progressively remove the residual stresses, and characterize their effects on magnetic behaviour and MFL signals. In a second series of experiments we simulated mechanical damage using a tool and die press to progressively ‘dent’ a number of plate samples. As with true mechanical damage, the resulting MFL signals arise from both geometrical and residual stress effects. Subsequent stress relieving heat treatments were used to separate and compare the ‘geometrical’ MFL signal from the ‘residual stress’ MFL signal.

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

Alternative magnetic flux leakage modalities for pipeline inspection

1996 ◽  
Vol 32 (3) ◽  
pp. 1581-1584 ◽  
Author(s):  
G. Katragadda ◽  
W. Lord ◽  
Y.S. Sun ◽  
S. Udpa ◽  
L. Udpa
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Pipeline Inspection ◽  
Flux Leakage
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