Study on Working Stress Measurement Method for Steel Bars inside RC Bridges Based on Self-magnetic Flux Leakage Spatial Signals

Measurement ◽  
2021 ◽  
pp. 109371
Author(s):  
Yinghao Qu ◽  
Hong Zhang ◽  
Ruiqiang Zhao ◽  
Lei Fu ◽  
Jianting Zhou
Keyword(s):  
Magnetic Flux ◽  
Measurement Method ◽  
Stress Measurement ◽  
Magnetic Flux Leakage ◽  
Working Stress ◽  
Steel Bars ◽  
Flux Leakage

scholarly journals Quantitative Evaluation of Corrosion Degrees of Steel Bars Based on Self-Magnetic Flux Leakage

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 952
Author(s):  
Ding Yang ◽  
Junli Qiu ◽  
Haibo Di ◽  
Siyu Zhao ◽  
Jianting Zhou ◽  
...  
Keyword(s):  
Experimental Data ◽  
Magnetic Flux ◽  
Mathematic Model ◽  
Magnetic Flux Leakage ◽  
Rc Structures ◽  
Nondestructive Tests ◽  
Magnetization State ◽  
Initial Magnetization ◽  
Steel Bars ◽  
Flux Leakage

Corrosion is among the most critical factors leading to the failure of reinforced concrete (RC) structures. Less work has been devoted to nondestructive tests (NDT) to detect the corrosion degree of steel bars. The corrosion degree was investigated in this paper using an NDT method based on self-magnetic flux leakage (SMFL). First, a mathematic model based on magnetic dipole model was settled to simulate the SMFL of a V-shaped defect caused by corrosion. A custom 3-axis scanning device equipped with a magnetometer was used to scan the SMFL field of the 40 corroded steel bars. Experimental data obtained by scanning the 40 steel bars showed that the BZ curve of SMFL was consistent with the theoretical model analysis. Inspired by the qualitative analysis of the results, an index “K” based on a large number of experimental data was established to characterize the corrosion degree of steel bars. The experimental index “K” was linearly related to the corrosion degree α of steel bars. This paper provides a feasible approach for the corrosion degree NDT, which is not affected by the magnetization history and the initial magnetization state of steel bars.

scholarly journals Non-Destructive Testing of Steel Corrosion Fluctuation Parameters Based on Spontaneous Magnetic Flux Leakage and Its Relationship with Steel Bar Diameter

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4116 ◽  
Author(s):  
Qingyuan Zhao ◽  
Jianting Zhou ◽  
Qianwen Xia ◽  
Senhua Zhang ◽  
Hong Zhang
Keyword(s):  
Magnetic Flux ◽  
Steel Corrosion ◽  
Magnetic Flux Leakage ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Detection Technology ◽  
Steel Bar ◽  
Steel Bars ◽  
Non Destructive ◽  
Flux Leakage

In an actual structure, the arrangement of steel bars is complicated, there are many factors affecting the corrosion of steel bars, and these factors affect each other. However, accurately reflecting the corrosion of steel bars in actual engineering through theoretical calculations is difficult. Besides, it is impossible to detect and evaluate steel bars rust completely and accurately. This article is based on spontaneous magnetic leakage detection technology and adopts the method of stage corrosion and scanning along the reinforcing bar. Based on spontaneous magnetic flux leakage detection technology, the linear change rate of the tangential component curve of the magnetic flux leakage signal generated after the corrosion of a steel bar is studied, and a comparison is made between the steel bar coated concrete samples with different steel bar diameters. In this paper, the “origin of magnetic flux leakage signal” is defined as a reference point, which is convenient for effectively comparing the magnetic signal curves under all operating conditions. Besides, the “rust-magnetic fluctuation parameter” is proposed to accurately reflect the sudden change of leakage magnetic field caused by disconnection due to the corrosion of a steel bar. A new data processing method is provided for the non-destructive testing of steel corrosion using the spontaneous magnetic flux leakage effect, which can effectively reduce the influence of steel bar diameter on magnetic flux leakage signal and improve the precision of non-destructive testing technology of steel bar corrosion using the metal magnetic memory effect.

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
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