Magnetic-Flux-Leakage-Based Pressure Deformation Detection of Elevator Wire Rope

Yu, Byoungjoon; Kim, Ju-won; Park, Seunghee

doi:10.7779/jksnt.2017.37.4.269

Cited by 4 publications

(2 citation statements)

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“…He designed and manufactured a 4-channel MFL sensor head. Experiments have verified the feasibility of the wire rope damage detection technology [7]. Fontanari et al have carried out research on lightning damage of aerial ropeway full-lock steel ropes.…”

Section: Introductionmentioning

confidence: 96%

Nondestructive Testing of Coal Mine Wire Ropes Based on Magnetic Sensors

Yuan

Wang

Chen

et al. 2022

International Transactions on Electrical Energy Systems

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Safety is the first priority in production. The situation is complex in the deep underground of coal mines, and safety accidents often occur. Wire rope is a kind of safety device. With the help of wire rope, the safety of miners can be guaranteed to a certain extent. It is a very important tool for mining production. However, the current detection of coal mine wire rope still relies on the data from the factory, and it is impossible to achieve nondestructive testing in use. This article aims to study the degree of wear of mining wire ropes to ensure the safety of miners as much as possible. According to the characteristics of the wire rope, any defect (rust, broken wire, broken wire, etc.) changes in the cross-sectional area of the wire rope will change the magnetic permeability of the wire rope. With the help of equipment, we can collect data on changes in the magnetic field (MF). Based on this experimental design, the steel wire rope is given an MF, and the magnetic sensor is used to monitor the MF signal and convert it into an electrical signal. According to the collected electrical signal analysis and judgment, the sensor model is constructed, the size and position of the wire rope cross-sectional change are deduced, and the defect of the wire rope is judged. The experimental results in this study show that the magnetic sensor is an effective measure to monitor the nondestructive degree of the wire rope, with a sensitivity of more than 80%.

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Section: Introductionmentioning

confidence: 96%

Nondestructive Testing of Coal Mine Wire Ropes Based on Magnetic Sensors

Yuan

Wang

Chen

et al. 2022

International Transactions on Electrical Energy Systems

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“…Hidden layer Output layer efficient nondestructive testing technique for the defected wire rope and plays an important role in the dynamic monitoring of wire rope [26][27][28]. Because of the intricate structure of wire rope, there is a complicated relation between the diverse damages and MFL signals.…”

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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Multi-loop magnetisation method for detecting coal mine wire rope defects

Wang¹,

Tian²,

Meng³

et al. 2020

insight

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Existing methods for detecting magnetic leakage signals from damaged wire ropes require axial saturation magnetisation and the accuracy of the detection equipment depends on the degree of saturation of the magnetisation. Moreover, owing to the special characteristics of the structure, diameter and operating environment of the wire rope, it is difficult to achieve the expected magnetisation effect. Consequently, in addition to other issues, the detection accuracy and versatility of the equipment are low. This study proposes a method based on spatial multi-dimensional orthogonal array loop magnetisation to detect coal mine wire rope defects. First, a sensor excitation structure model is developed. Then, a method for detecting the radial magnetic flux using permanent magnet arrays is analysed and the influence of the number of permanent magnet radial magnetic blocks on the magnetisation effect is studied. In addition, a wire rope detection method based on the principle of clustering is investigated and used to discuss the influence of the shape, structure and size of the sensor on the magnetisation effect. Finally, through laboratory experiments, the applicability of the test model and the proposed method are verified. The results show that the magnetisation effect is better and more cost-effective when the number of permanent magnet radial magnetic blocks is eight and that the proposed detection method can effectively identify damaged wire rope joints. Furthermore, the proposed equipment achieves a signal-tonoise ratio of 28 dB, thereby improving the reliability of damage detection.

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Magnetic-Flux-Leakage-Based Pressure Deformation Detection of Elevator Wire Rope

Cited by 4 publications

References 0 publications

Nondestructive Testing of Coal Mine Wire Ropes Based on Magnetic Sensors

Nondestructive Testing of Coal Mine Wire Ropes Based on Magnetic Sensors

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

Multi-loop magnetisation method for detecting coal mine wire rope defects

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