Detection Technology of Mine Wire Rope Based on Radial Magnetic Vector With Flexible Printed Circuit

Jing, Tian; Zhao, Caiyue; Wang, Wei; Sun, Ganggang

doi:10.1109/tim.2021.3096288

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…According to the different scanning directions, the installation direction of the Hall-effect sensor is divided into circumferential, radial, and axial, as shown in Figure 2 . Among them, the radial installation has a smaller lift-off value compared with the axial or circumferential Hall-effect sensor [ 18 ].…”

Section: Magnetic Field Modeling and Analysismentioning

confidence: 99%

Enhancing Wire-Rope Damage Signals Based on a Radial Magnetic Concentrator Bridge Circuit

Jing

Wang

et al. 2022

Sensors

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Hall-effect sensors are used for the non-destructive testing of wire ropes owing to their low power consumption and high operation frequency. The high-speed operation of wire ropes causes vibration inclination at different frequencies, which makes it difficult to detect the ropes. Considering that the radial signal in the magnetic flux leakage (MFL) detection method can respond to damages to the maximum extent possible, this study proposes a radial magnetic concentrator suitable for the non-destructive testing of wire ropes based on theoretical analysis and transient magnetic field simulations. The concentrator improves the radial magnetic circuit, polymerizes the leakage of the magnetic field in the detection device, and the leakage of the magnetic field of the defect converges at the sensor position of the circumferential array to improve the signal-to-noise ratio of the Hall-effect sensor. In addition, the MFL field is homogenized through the structure of the magnetic concentrator when the wire rope is tilted, which weakens the influence of the vibration tilt of the wire rope on the test results. Finally, the experiments show that the amplitude of the wire-rope damage signal is effectively improved by using the proposed radial magnetic concentration technology, hence being convenient for defect analyses.

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Section: Magnetic Field Modeling and Analysismentioning

confidence: 99%

Enhancing Wire-Rope Damage Signals Based on a Radial Magnetic Concentrator Bridge Circuit

Jing

Wang

et al. 2022

Sensors

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“…Wire ropes, as a key component of mine hoists, are subjected to vibration impact under the high speed and heavy loads of hoisting systems 1,2,3 . In mining operations, wire ropes are subjected to prolonged stretching, bending, and alternating loads 4,5 , which inevitably produce defects and damage, such as broken wires, deformation, and missing strands; this damage increases greatly with time 6,7,8 . The high probability of safety accidents caused by wire rope damage poses a serious threat to the safety of operators and mining equipment and adversely affects the productivity of mining operations 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive Threshold and Weighted Frequency Domain Histogram of Local Binary Patterns

Qi,

Wang,

Liu

2024

ETJ

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Wire ropes are crucial load-bearing components in mining conveyance equipment, and machine vision is one of the methods used to assess the surface damage condition of wire ropes. In response to the light-sensitive nature of local binary patterns, which leads to issues such as differing feature values for similar textures and susceptibility to the influence of excessively large or small pixels within local windows, hindering the accurate reflection of window structure information and exacerbating the introduction of considerable feature noise, an investigation is conducted. To enhance the gradient structural information among pixels within local pixel window, an adaptive threshold binary pattern feature operator is proposed. This operator utilizes the mean and variance within the local window to balance the central pixel value, thereby enhancing the interconnection among neighboring pixels. To perform feature selection on block histograms, a block-weighted approach is employed. This approach utilizes the concept of block weighting and employs correlation coefficients to preprocess feature vectors, thereby enhancing classification accuracy. The algorithm experiments were conducted on a dataset of mine wire ropes. The results indicate that the improved local binary pattern significantly enhances the classification accuracy of the wire rope dataset, achieving an accuracy of 97.3%.

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“…Due to the harsh working environment of the mine, in the process of long-term use, it is inevitable that different damages will occur under the long-term influence of tensile bending, alternating loads, environmental corrosion, and other factors, resulting in safety accidents. When using steel wire rope, attention needs to be paid to its regular damage detection to ensure that it will not be broken or damaged in the process of use and to protect the safety and service life of steel wire rope [4][5][6][7]. Therefore, how to detect wire rope damage accurately and efficiently is the focus of the current research field on wire rope.…”

Section: Introductionmentioning

confidence: 99%

Research on Internal Damage Identification of Wire Rope Based on Improved VGG Network

Li,

Tian

2024

Entropy

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In order to solve the problem of great difficulty in detecting the internal damage of wire rope, this paper proposes a method to improve the VGG model to identify the internal damage of wire rope. The short-time Fourier transform method is used to transform the wire rope damage signal into a time-frequency spectrogram as the model input, and then the traditional VGG model is improved from three aspects: firstly, the attention mechanism module is introduced to increase the effective feature weights, which effectively improves the recognition accuracy; and then, the batch normalization layer is added to carry out a uniform normalization of the data, so as to make the model easier to converge. At the same time, the pooling layer and the fully connected layer are improved to solve the redundancy problem of the traditional VGG network model, which makes the model structure more lightweight, greatly saves the computational cost, shortens the training time, and finally adopts the joint-sample uniformly distributed cross-entropy as the loss function to solve the overfitting problem and further improve the recognition rate. The experimental results show that the improved VGG model has an identification accuracy of up to 98.84% for the internal damage spectrogram of the wire rope, which shows a good identification ability. Not only that, but the model is also superior, with less time-consuming training and stronger generalization ability.

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Detection Technology of Mine Wire Rope Based on Radial Magnetic Vector With Flexible Printed Circuit

Cited by 14 publications

References 17 publications

Enhancing Wire-Rope Damage Signals Based on a Radial Magnetic Concentrator Bridge Circuit

Enhancing Wire-Rope Damage Signals Based on a Radial Magnetic Concentrator Bridge Circuit

Adaptive Threshold and Weighted Frequency Domain Histogram of Local Binary Patterns

Research on Internal Damage Identification of Wire Rope Based on Improved VGG Network

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