Studies on the Spatial Location Method of Corrosion Defect in Bridge Cable Considering Self-Magnetic Flux Leakage Effect

2021 ◽  
Vol 50 (1) ◽  
pp. 20200367
Author(s):  
Runchuan Xia ◽  
Hong Zhang ◽  
Jianting Zhou ◽  
Leng Liao ◽  
Haibo Di ◽  
...  
Keyword(s):  
Magnetic Flux ◽  
Spatial Location ◽  
Magnetic Flux Leakage ◽  
Location Method ◽  
Corrosion Defect ◽  
Leakage Effect ◽  
Bridge Cable ◽  
Flux Leakage

Finite Element Analysis and Research on Corrosion Defect of Tank Floor

2016 ◽  
Vol 853 ◽  
pp. 514-518
Author(s):  
Zhi Jun Yang ◽  
De Shu Chen ◽  
Liang Chen ◽  
Yu Zhuo Liu ◽  
Ran An ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Finite Element ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Bottom Plate ◽  
Element Analysis ◽  
Corrosion Defect ◽  
Leakage Magnetic Field ◽  
Leakage Field ◽  
Flux Leakage

Storage tank is an essential vessel in petrochemical industry, and the corrosion of tank is an important reason for the safety hazard. The corrosion of tank bottom plate is more serious than the tank wall, and it is not easy to check and repair, when damaged to a certain extent it will cause the leakage of the media, then lead to waste of energy, environmental pollution, at the same time it will cause a major accident. Magnetic flux leakage testing is widely used in the field of tank floor inspection with the advantages of fast scanning speed, accurate results and so on. In this paper, the finite element simulation and analysis of the corrosion defect leakage magnetic field is used to obtain the data, and the characteristic of the leakage magnetic field is extracted. The effect of defect depth and width and shape on the magnetic flux leakage field is studied, and the distribution curve of the magnetic flux leakage field is obtained.

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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