Wire Rope Damage Detection Signal Processing Using K-Singular Value Decomposition and Optimized Double-Tree Complex Wavelet Transform

Wang, Hongyao; Li, Qinyi; Han, Shuang; Li, Pengbo; Jing, Tian; Zhang, Shijing

doi:10.1109/tim.2022.3216670

Cited by 5 publications

(1 citation statement)

References 18 publications

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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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Section: Introductionmentioning

confidence: 99%