Paper Info
Title
Real-Time Inspection in Detection Magnetic Flux Leakage by Deep Learning Integrated with Concentrating Non-Destructive Principle and Electromagnetic Induction
Abstract
One of the most common techniques of pipeline inspection is magnetic flux leakage (MFL). It is a non-destructive testing (NDT) method that employs magnetic sensitive sensors to detect MFL of faults on pipelines' internal and external surfaces. This research proposed a novel technique in real-time detection of MFL with pattern recognition in non-destructive principle using deep learning architectures. Here, the MFL signal has been collected as a large data sequence which has to be trained and validated using neural networks. Initially, the MFL has been detected using Faraday's law of electromagnetic induction (EMI) which is induced with Z-filter in electromagnetic (EM) decomposition. The collected signal of MFL has been classified using convolutional neural network (CNN), and this classified signal has been recognized by the patterns based on their threshold of the signal. By extracting and analyzing magnetic properties of MFL for a signal, the quantitative MFL has exceeded their threshold value from detected signals. Damage indices based on the link between enveloped MFL signal and the threshold value, as well as a generic damage index for MFL technique, were used to strengthen the quantitative analysis.
Year
DOI
Venue
2022
10.1109/MIM.2022.9908257
IEEE Instrumentation & Measurement Magazine
Keywords
DocType
Volume
Magnetic flux leakage, Pipelines, Neural networks, Electromagnetic induction, Inspection, Real-time systems, Pattern recognition
Journal
25
Issue
ISSN
Citations 
7
1094-6969
0
PageRank 
References 
Authors
0.34
3
7
Name
Order
Citations
PageRank
Nallamilli P.G. Bhavani100.34
Ganapathy Senthilkumar200.34
Shahnazeer Chembalakkat Kunjumohamad300.34
Azhagu Jaisudhan Pazhani400.34
Ravi Kumar5214.89
Abolfazl Mehbodniya600.34
Julian L. Webber700.34