Magnetic flux leakage-based steel cable NDE and damage visualization on a cable climbing robot

2012 ◽  
Author(s):  
Ju-Won Kim ◽  
Changgil Lee ◽  
Seunghee Park ◽  
Jong Jae Lee
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Climbing Robot ◽  
Steel Cable ◽  
Damage Visualization ◽  
Flux Leakage

Magnetic Flux Leakage Sensing-Based Steel Cable NDE Technique Incorporated on a Cable Climbing Robot for Bridge Structures

2012 ◽  
Vol 83 ◽  
pp. 217-222 ◽  
Author(s):  
Seung Hee Park ◽  
Ju Won Kim ◽  
Min Jun Nam ◽  
Jong Jae Lee
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Magnetic Flux Density ◽  
Inspection System ◽  
Climbing Robot ◽  
Steel Cable ◽  
Monitoring Method ◽  
Sensor Head ◽  
Bridge Structures ◽  
Flux Leakage

In this study, an automated cable monitoring system using a NDE technique and a cable climbing robot is proposed. MFL (Magnetic Flux Leakage- based inspection system was applied to monitor the condition of cables. This inspection system measures magnetic flux to detect the local faults (LF) of steel cable. To verify the feasibility of the proposed damage detection technique, an 8-channel MFL sensor head prototype was designed and fabricated. A steel cable bunch specimen with several types of damage was fabricated and scanned by the MFL sensor head to measure the magnetic flux density of the specimen. To interpret the condition of the steel cable, magnetic flux signals were used to determine the locations of the flaws and the level of damage. Measured signals from the damaged specimen were compared with thresholds set for objective decision making. In addition, the measured magnetic flux signal was visualized into a 3D MFL map for convenient cable monitoring. Finally, the results were compared with information on actual inflicted damages to confirm the accuracy and effectiveness of the proposed cable monitoring method.

scholarly journals Magnetic Flux Leakage Sensing-Based Steel Cable NDE Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Seunghee Park ◽  
Ju-Won Kim ◽  
Changgil Lee ◽  
Jong-Jae Lee
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Magnetic Flux Density ◽  
Inspection System ◽  
Steel Cable ◽  
Monitoring Method ◽  
Sensor Head ◽  
Long Span Bridges ◽  
Long Span ◽  
Flux Leakage

Nondestructive evaluation (NDE) of steel cables in long span bridges is necessary to prevent structural failure. Thus, an automated cable monitoring system is proposed that uses a suitable NDE technique and a cable-climbing robot. A magnetic flux leakage- (MFL-) based inspection system was applied to monitor the condition of cables. This inspection system measures magnetic flux to detect the local faults (LF) of steel cable. To verify the feasibility of the proposed damage detection technique, an 8-channel MFL sensor head prototype was designed and fabricated. A steel cable bunch specimen with several types of damage was fabricated and scanned by the MFL sensor head to measure the magnetic flux density of the specimen. To interpret the condition of the steel cable, magnetic flux signals were used to determine the locations of the flaws and the levels of damage. Measured signals from the damaged specimen were compared with thresholds that were set for objective decision-making. In addition, the measured magnetic flux signals were visualized as a 3D MFL map for intuitive cable monitoring. Finally, the results were compared with information on actual inflicted damages, to confirm the accuracy and effectiveness of the proposed cable monitoring method.

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.

Sign in / Sign up

Export Citation Format

Share Document