MODELING AND RESULTS FOR CREATING OBLIQUE FIELDS IN A MAGNETIC FLUX LEAKAGE SURVEY TOOL

2010 ◽  
Author(s):  
James C. Simek ◽  
Donald O. Thompson ◽  
Dale E. Chimenti
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Survey Tool ◽  
Flux Leakage

Oblique Field Magnetic Flux Leakage Inline Survey Tool: Implementation and Results

2010 ◽  
Author(s):  
James Simek ◽  
Jed Ludlow ◽  
Phil Tisovec
Keyword(s):  
Experimental Data ◽  
Magnetic Flux ◽  
Full Range ◽  
Magnetic Flux Leakage ◽  
Metal Loss ◽  
Data Sets ◽  
Data Set ◽  
Survey Tool ◽  
Oblique Field ◽  
Flux Leakage

InLine Inspection (ILI) tools using the magnetic flux leakage (MFL) technique are the most common type used for performing metal loss surveys worldwide. Based upon the very robust and proven magnetic flux leakage technique, these tools have been shown to operate reliably in the extremely harsh environments of transmission pipelines. In addition to metal loss, MFL tools are capable of identifying a broad range of pipeline features. Most MFL surveys to date have used tools employing axially oriented magnetizers, capable of detecting and quantifying many categories of volumetric metal loss features. For certain classes of axially oriented features, MFL tools using axially oriented fields have encountered difficulty in detection and subsequent quantification. To address features in these categories, tools employing circumferential or transversely oriented fields have been designed and placed into service, enabling enhanced detection and sizing for axially oriented features. In most cases, multiple surveys are required, as current tools do not incorporate the ability to collect both data sets concurrently. Applying the magnetic field in an oblique direction will enable detection of axially oriented features and may be used simultaneously with an axially oriented tool. Referencing previous research in adapting circumferential or transverse designs for inline service, the concept of an oblique field magnetizer will be presented. Models developed demonstrating the technique are discussed, shown with experimental data supporting the concept. Efforts involved in the implementation of an oblique magnetizer, including magnetic models for field profiles used to determine magnetizer configurations and sensor locations are presented. Experimental results are provided detailing the response of the system to a full range of metal loss features, supplementing modeling in an effort to determine the effects of variables introduced by magnetic property and velocity induced differences. Included in the experimental data results are extremely narrow axially oriented features, many of which are not detected or identified within the axial data set. Experimental and field verification results for detection accuracies will be described in comparison to an axial field tool.

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

GMR-based magnetic flux leakage technique for condition monitoring of steel track rope

2011 ◽  
Vol 53 (7) ◽  
pp. 377-381 ◽  
Author(s):  
W Sharatchandra Singh ◽  
B P C Rao ◽  
C K Mukhopadhyay ◽  
T Jayakumar
Keyword(s):  
Magnetic Flux ◽  
Condition Monitoring ◽  
Magnetic Flux Leakage ◽  
Flux Leakage
Sign in / Sign up

Export Citation Format

Share Document