Detection of high stress concentration zone using magnetic flux leakage method

2020 ◽  
Vol 11 (4) ◽  
pp. 615-624
Author(s):  
Syed Muhamad Firdaus ◽  
Azli Arifin ◽  
Siti Norbaya Sahadan ◽  
Shahrum Abdullah
Keyword(s):  
Magnetic Flux ◽  
High Stress ◽  
Magnetic Flux Leakage ◽  
Stress Concentration Zone ◽  
Concentration Zone ◽  
Content Type ◽  
High Stress Concentration ◽  
Tower Crane ◽  
The Relationship ◽  
Flux Leakage

PurposeA tower crane mainly ensures the success or efficiency of building construction. Fatigue crack analysis is important for tower crane components to prevent any accidents to workers in construction sites caused by component failure and to ease the maintenance or replacement of failed components. This work aimed to characterise the damage of failed components, analyse the relationship between the metal magnetic memory (MMM) result and the damage of failed components, and to validate the relationship between MMM and finite element analysis (FEA).Design/methodology/approachMMM was used in this work to detect any irregularities or early failure on the basis of the high stress concentration zone of ferromagnetic steel using magnetic flux leakage. Magnetic flux leakage was used on the MMM device to achieve the first objective using the MMM system by detecting the irregularities. The results of MMM analysis were validated through comparison with FEA results by determining their relationship.FindingsMMM results show that the position of defects on the tower crane pulley is within the stress area shown on FEA.Originality/valueHence, MMM method is a potential tool in monitoring failure mechanism in construction site.

Stress Induced Self Magnetic Flux Leakage at Stress Concentration Zone

2021 ◽  
pp. 1-1
Author(s):  
Mehrdad Kashefi ◽  
Lynann Clapham ◽  
Thomas W. Krause ◽  
P. Ross Underhill ◽  
Anthony K. Krause
Keyword(s):  
Stress Concentration ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Stress Concentration Zone ◽  
Concentration Zone ◽  
Flux Leakage

Signal Acquisition Analysis in Hi-Speed and Hi-Precision MFL Testing for Steel Pipe

2013 ◽  
Vol 718-720 ◽  
pp. 875-880 ◽  
Author(s):  
Jian Bo Wu ◽  
Jun Tu ◽  
Yun Yang ◽  
Yi Hua Kang
Keyword(s):  
Magnetic Flux ◽  
Sampling Frequency ◽  
Magnetic Flux Leakage ◽  
Steel Pipe ◽  
Signal Frequency ◽  
Wave Form ◽  
Signal Acquisition ◽  
Sample Frequency ◽  
The Relationship ◽  
Flux Leakage

Dealing with the relationship properly between the sensor scanning and signal acquisition is the base of hi-speed and hi-precision MFL (magnetic flux leakage) testing for steel pipe. Firstly, the MFL wave form characteristic was established using magnetic dipole theory. Further, on that basis, the relationship between signal frequency and sensor scanning was analyzed. Finally, sample frequency was designed according to the requirement of the automatic steel pipe MFL testing. Additionally, the MFL signal acquisition experiment was conducted to verify the influence of the signal sampling frequency. The signal acquisition analysis was of great significant to perform the MFL testing for steel pipe in hi-speed and hi-precision.

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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