Steel Coiled Tubing Defect Evaluation Using Magnetic Flux Leakage Signals

2014 ◽  
Author(s):  
Zhanke Liu ◽  
Gerald Minerbo ◽  
Andrew Zheng
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Coiled Tubing ◽  
Flux Leakage

Novel Application of Magnetic Flux Leakage-Based Technology Validating Pipe Elongation in Coiled Tubing Drilling Operation

2020 ◽  
Author(s):  
Kevin Wiranata ◽  
Arafet Ridene ◽  
Mohd Nur Afendy Md Yusoff ◽  
Abdullah Hamdi ◽  
Rodrigo Sa ◽  
...  
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Drilling Operation ◽  
Coiled Tubing ◽  
Flux Leakage

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.

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