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

Guoxin Sun

Guoxin Sun

  • School of Mechanical Engineering, Inner Mongolia University of Science and TechnologyBaotou, China
  • Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems, Inner Mongolia Autonomous RegionBaotou, China
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Sun, Guoxin
, 
Xinpeng Du

Xinpeng Du

School of Mechanical Engineering, Inner Mongolia University of Science and TechnologyBaotou, China
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Du, Xinpeng
, 
Jianlong Zhang

Jianlong Zhang

North Intelligent Energy Research InstituteMingyang, Inner Mongolia
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Zhang, Jianlong
, 
Runze Zhang

Runze Zhang

School of Mechanical Engineering, Inner Mongolia University of Science and TechnologyBaotou, China
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Zhang, Runze
 oraz 
Qihui Yu

Qihui Yu

School of Mechanical Engineering, Inner Mongolia University of Science and TechnologyBaotou, China
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Yu, Qihui
  
19 sty 2025
Quantitative identification of wire rope core conveyor belt damage based on GWO-BP's Cover Image
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Data publikacji: 19 sty 2025
Otrzymano: 11 wrz 2024
DOI: https://doi.org/10.2478/ijssis-2025-0003
Słowa kluczowe
© 2025 Guoxin Sun et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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 number Experiment instrument Quantity Model Rated voltage
1 Excitation device 2 RC300 /
2 Speed sensor 1 GS10 (A) DC12V
3 Injury detection sensors 2 GTSC300 DC5V
4 Digital mining conversion workstation 1 TCK.W-AI-E9 AC220V
5 Terminal master control unit 1 TCK.W-ZK1200-D AC127V
Język:
Angielski
Częstotliwość wydawania:
1 razy w roku
Dziedziny czasopisma:
Inżynieria, Wstępy i przeglądy, Inżynieria, inne
Kanał RSS czasopisma