Signal extraction using complementary ensemble empirical mode in pipeline magnetic flux leakage nondestructive evaluation

2019 ◽  
Vol 90 (7) ◽  
pp. 075101
Author(s):  
Mingjiang Shi ◽  
Honghui Zhao ◽  
Zhiqiang Huang ◽  
Qin Liu
Keyword(s):  
Magnetic Flux ◽  
Nondestructive Evaluation ◽  
Magnetic Flux Leakage ◽  
Signal Extraction ◽  
Flux Leakage

A magnetic flux leakage method using a magnetoresistive sensor for nondestructive evaluation of spot welds

2011 ◽  
Vol 44 (1) ◽  
pp. 101-105 ◽  
Author(s):  
Keiji Tsukada ◽  
Mitsuteru Yoshioka ◽  
Toshihiko Kiwa ◽  
Yoshinobu Hirano
Keyword(s):  
Magnetic Flux ◽  
Nondestructive Evaluation ◽  
Magnetic Flux Leakage ◽  
Spot Welds ◽  
Magnetoresistive Sensor ◽  
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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