The effect of variable tensile stress on the MFL signal response of defective wire ropes

Gao, Guohua; Lian, Meijuan; Xu, Yan; Qin, Yanan; Lin, Gao

doi:10.1784/insi.2016.58.3.135

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…With the proposed model, Wang showed that the MFL signal increases with the increase in stress in the Q235 steel plate. Gao also observed a similar effect in the testing of steel wire ropes [ 83 ]. Later, Shi showed that the change in the MFL signal behaves differently in the elastic and plastic deformation stage [ 84 ].…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 65%

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A Review of Magnetic Flux Leakage Nondestructive Testing

Feng

et al. 2022

Materials

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Magnetic flux leakage (MFL) testing is a widely used nondestructive testing (NDT) method for the inspection of ferromagnetic materials. This review paper presents the basic principles of MFL testing and summarizes the recent advances in MFL. An analytical expression for the leakage magnetic field based on the 3D magnetic dipole model is provided. Based on the model, the effects of defect size, defect orientation, and liftoff distance have been analyzed. Other influencing factors, such as magnetization strength, testing speed, surface roughness, and stress, have also been introduced. As the most important steps of MFL, the excitation method (a permanent magnet, DC, AC, pulsed) and sensing methods (Hall element, GMR, TMR, etc.), have been introduced in detail. Finally, the algorithms for the quantification of defects and the applications of MFL have been introduced.

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Section: Factors Influencing Mfl Signalmentioning

confidence: 65%

“…The stress effect has been studied by many researchers [ 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. The properties of ferromagnetic materials change with the loading stress due to the magneto-mechanical coupling, thus the MFL signal also changes with the stress.…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

A Review of Magnetic Flux Leakage Nondestructive Testing

Feng

et al. 2022

Materials

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“…To further explain the formation principle of MFL, Sun et al [19] explained the mechanism of MFL in engineering from the perspective of magnetic refraction, and found that the signal component in defect detection was different from that in traditional MFL analysis, which was produced by protruding defects. Considering the effect of tensile stress on the strength of the MLF of a steel WR defect, Gao et al [20] loaded a variable force onto reproduced samples of typical defective WRs relying on an inspection platform. The experimental results showed that the peak-to-peak values of the MFL signal from all defective WRs increased with tensile stress, in an approximately linear relationship.…”

Section: Mechanism and Modelmentioning

confidence: 99%

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Zhou

Zhu

et al. 2019

Applied Sciences

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As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

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“…erefore, it is an important and urgent lesson in the research field that explains how to apply the simplest and fastest method for fault feature extraction of the broken wire of wire rope. e effect of variable tensile stress on the MFL signal response of defective wire ropes is analyzed and dealt with as needed [31]. e filtering system consisting of the Hilbert-Huang transform and compressed sensing is used to obtain the defect RMF image characteristics of wire rope, and the characteristics are extracted as the input of a radial basis function neural network to identify the defects of wire rope [32].…”

Section: Input Layermentioning

confidence: 99%

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

Zhao

Zhang

2018

Shock and Vibration

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An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI-ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI-ELM model, respectively. The experimental results show that the VSI-ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI-ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI-ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

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The effect of variable tensile stress on the MFL signal response of defective wire ropes

Cited by 8 publications

References 12 publications

A Review of Magnetic Flux Leakage Nondestructive Testing

A Review of Magnetic Flux Leakage Nondestructive Testing

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

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