Application of Zero-Phase Digital Filter in Magnetic Flux Leakage Testing for Tank Floor Inspection

2013 ◽  
Vol 333-335 ◽  
pp. 1644-1648
Author(s):  
Fu Jun Liu ◽  
Zhang Wei Ling ◽  
Shuai Kong ◽  
Mu Lin Zheng
Keyword(s):  
Magnetic Flux ◽  
Digital Filter ◽  
Signal To Noise Ratio ◽  
Phase Distortion ◽  
Magnetic Flux Leakage ◽  
Location Accuracy ◽  
Phase Filter ◽  
Testing Technology ◽  
Flux Leakage ◽  
Zero Phase

Magnetic flux leakage (MFL) testing, as a modern nondestructive testing technology, has significant application in tank floor corrosion defect inspection. In view of signal characteristics in the MFL testing of tank floor, an improved signal progressing method combining software and hardware was presented. Especially, in order to avoid phase distortion caused by applying common digital filter, zero-phase digital filter which could effectively eliminate the phase distortion was proposed to improve the location accuracy of defect. Experiment results showed that the signal progressing method could improve signal-to-noise ratio, and the peak-peak value of defect signal had a good linear relationship with the depth of defect which showed that the proposed method could meet the requirement of the MFL testing. Comparing results showed that, the maximal location error of zero-phase filter was 2 mm and that of common digital filter was 24 mm, which showed that zero-phase filter had better defect location accuracy.

Numerical Simulation Study of Stress-Magnetization Effect on Different Notch Steel Specimens

2014 ◽  
Vol 989-994 ◽  
pp. 891-897 ◽  
Author(s):  
Xiao Wen Xi ◽  
Shang Kun Ren ◽  
Yin Huang
Keyword(s):  
Magnetic Flux ◽  
Quantitative Research ◽  
Tensile Load ◽  
Magnetic Flux Leakage ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Element Analysis ◽  
Damage Degree ◽  
Testing Technology ◽  
Flux Leakage

To study the mechanism of metal magnetic memory (MMM) testing technology, the stress-magnetization effect on 20 steel specimens with different notch angles under exercise of the geomagnetic field and tensile load is simulated by using the finite element analysis (FEA) software ANSYS. With the stimulation, the stress and magnetic flux leakage distribution of the specimens is given. The results showed that internal stress distribution of different notch specimens under external tensile effects is different; The curves of relationship between damage degree of stress concentration and the distribution of magnetic flux leakage is also related to the defect shape and structure; Magnetization decreases with increases of stress at first and then increases with continuing increase of stress, which is called stress magnetization reversal. It provides an important reference for the quantitative research of metal magnetic memory technology.

The Design and Implementation of Adaptive Noise Cancellation for Magnetic Flux Leakage Test Signal

2013 ◽  
Vol 441 ◽  
pp. 393-396
Author(s):  
Xiao Gang Han ◽  
Mei Quan Liu ◽  
Qin Lei Sun
Keyword(s):  
Magnetic Flux ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Magnetic Flux Leakage ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Leakage Test ◽  
Adaptive Noise ◽  
Flux Leakage

In the application of magnetic flux leakage (MFL) nondestructive testing, the signal will be disturbed by varying noises. It seriously affects the accuracy of detection judgment result. This paper describes the design of least mean square (LMS) noise cancellation for MFL signal and the implementation based on ARM platform. Two giant magnetoresistive sensors are used to measure the signal, one sensor for MFL signal (with noise) and the other one for the noise signal. They are inputted to the noise cancellation to obtain pure MFL signals. Experimental results show that the LMS noise cancellation significantly improves the signal to noise ratio.

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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