Magnetic Flux Leakage Detection Technology for Well Casing on Neural Network

2008 ◽  
Author(s):  
Jinzhong Chen ◽  
Lin Li ◽  
Jinan Shi
Keyword(s):  
Neural Network ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Leakage Detection ◽  
Detection Technology ◽  
Flux Leakage

Finite Element Analysis and Experimental Research for Horizontal Tank to Internal Magnetic Flux Leakage Detection

2014 ◽  
Vol 599-601 ◽  
pp. 321-325
Author(s):  
Li Qiang Sun ◽  
Hong Bo Zhu ◽  
Ming Xie ◽  
Ji Xia Li
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Leakage Detection ◽  
Element Analysis ◽  
The Finite Element Analysis ◽  
Horizontal Tank ◽  
Flux Leakage ◽  
Detection Effect

In view of the petroleum and petrochemical characteristics of horizontal tank, ANSYS software of finite element analysis was carried out on the horizontal tank within the magnetic flux leakage testing, analyzes the influencing factors of defect magnetic flux leakage signals. Experiments verify the finite element analysis results, the experimental results show that the research of horizontal tank within the magnetic flux leakage detection effect is obvious.

scholarly journals A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage

2021 ◽  
Vol 11 (20) ◽  
pp. 9489
Author(s):  
Yinliang Jia ◽  
Shicheng Zhang ◽  
Ping Wang ◽  
Kailun Ji
Keyword(s):  
Magnetic Flux ◽  
Surface Defects ◽  
Rapid Development ◽  
Magnetic Flux Leakage ◽  
Detection Accuracy ◽  
Physical Experiments ◽  
Detection Technology ◽  
Element Simulation ◽  
Lift Off ◽  
Flux Leakage

With the rapid development of the world’s railways, rail is vital to ensure the safety of rail transit. This article focuses on the magnetic flux leakage (MFL) non-destructive detection technology of the surface defects in railhead. A Multi-sensors method is proposed. The main sensor and four auxiliary sensors are arranged in the detection direction. Firstly, the root mean square (RMS) of the x-component of the main sensor signal is calculated. In the data more significant than the threshold, the defects are determined by the relative values of the sensors signal. The optimal distances among these sensors are calculated to the size of a defect and the lift-off. From the finite element simulation and physical experiments, it is shown that this method can effectively suppress vibration interference and improve the detection accuracy of defects.

scholarly journals Data Analysis of Magnetic Flux Leakage Detection Based on Multi-Source Information Fusion

2020 ◽  
Author(s):  
Weilin Shao ◽  
Ming Sun ◽  
Yilai Ma ◽  
Jinzhong Chen ◽  
Xiaowei Kang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Magnetic Flux ◽  
Information Fusion ◽  
Information Source ◽  
Magnetic Flux Leakage ◽  
Leakage Detection ◽  
Source Information ◽  
Source Data ◽  
Fusion Analysis ◽  
Flux Leakage

For the analysis of the magnetic flux leakage detection data in pipelines, a single information source data analysis method is used to determine the pipeline characteristics with uncertainty. A multi-source information fusion data analysis technology is proposed. This paper makes full use of the information collected by the multi-source sensors of the magnetic leakage internal detector, and adopts distributed and centralized multi-source information fusion analysis technology. First, pre-analyze and judge the information data of the auxiliary sensors (speed, pressure, temperature) of the internal magnetic flux leakage detector. Then, the data of the main sensor, ID / OD sensor, axial mileage sensor, and circumferential clock sensor of the magnetic flux leakage detector are analyzed separately. Finally, the RBF neural network + least squares support vector machine (LSSVM)fusion analysis technology is adopted to realize the fusion analysis of multi-source information. The results show that this method can effectively improve the quality and reliability of data analysis compared with traditional single information source data analysis.

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