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Quantitative identification of wire rope core conveyor belt damage based on GWO-BP Cover

Quantitative identification of wire rope core conveyor belt damage based on GWO-BP

International Journal on Smart Sensing and Intelligent Systems
Volume 18 (2025): Issue 1 (January 2025)
By:
Open Access
|Jan 2025

Figures & Tables

Figure 1:

Performance difference between GWO and PSO. GWO, grey wolf optimizer; PSO, particle swarm optimization.
Performance difference between GWO and PSO. GWO, grey wolf optimizer; PSO, particle swarm optimization.

Figure 2:

Hierarchy of grey wolves.
Hierarchy of grey wolves.

Figure 3:

Quantitative damage identification method for wire rope core conveyor belts based on GWO-BP. BPs, backpropagations; GWO, grey wolf optimizer.
Quantitative damage identification method for wire rope core conveyor belts based on GWO-BP. BPs, backpropagations; GWO, grey wolf optimizer.

Figure 4:

Optimization flowchart based on GWO-BP. BP, backpropagation; GWO, grey wolf optimizer.
Optimization flowchart based on GWO-BP. BP, backpropagation; GWO, grey wolf optimizer.

Figure 5:

Detection platform for internal damage in wire rope core conveyor belts.
Detection platform for internal damage in wire rope core conveyor belts.

Figure 6:

Samples of wire breakage damage.
Samples of wire breakage damage.

Figure 7:

Schematic diagram of feature values for steel cord damage signals.
Schematic diagram of feature values for steel cord damage signals.

Figure 8:

Local feature values of broken wires in the wire rope.
Local feature values of broken wires in the wire rope.

Figure 9:

Regression results of the dataset.
Regression results of the dataset.

Figure 10:

Classification errors of two prediction models. BP, backpropagation; GWO, grey wolf optimizer.
Classification errors of two prediction models. BP, backpropagation; GWO, grey wolf optimizer.

Figure 11:

Classification and recognition performance of two neural network prediction models.
Classification and recognition performance of two neural network prediction models.

Figure 12:

Recognition accuracy of two prediction models under different numbers of broken threads. BP, backpropagation; GWO, grey wolf optimizer.
Recognition accuracy of two prediction models under different numbers of broken threads. BP, backpropagation; GWO, grey wolf optimizer.

Figure 13:

Comparison of damage quantification identification between two prediction models. BP, backpropagation; GWO, grey wolf optimizer.
Comparison of damage quantification identification between two prediction models. BP, backpropagation; GWO, grey wolf optimizer.

Experimental instruments and main parameters

Serial numberExperiment instrumentQuantityModelRated voltage
1Excitation device2RC300/
2Speed sensor1GS10 (A)DC12V
3Injury detection sensors2GTSC300DC5V
4Digital mining conversion workstation1TCK.W-AI-E9AC220V
5Terminal master control unit1TCK.W-ZK1200-DAC127V
DOI: https://doi.org/10.2478/ijssis-2025-0003 | Journal eISSN: 1178-5608
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Language: English
Submitted on: Sep 11, 2024
Published on: Jan 19, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Steel wire rope core conveyor,
BP neural network,
Grey Wolf algorithm,
quantitative identification
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2025 Guoxin Sun, Xinpeng Du, Jianlong Zhang, Runze Zhang, Qihui Yu, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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