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Fault prediction and maintenance of urban rail transit power supply system based on big data

Wenfei Zhao

Wenfei Zhao

Urban Rail Transit and Information Technology College, Liuzhou Railway Vocational Technical CollegeLiuzhou, China
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Zhao, Wenfei
 e 
Jiao Deng

Jiao Deng

Guangxi Technician College of Machinery &ElectricityLiuzhou, China
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Deng, Jiao
  
17 mar 2025
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Pubblicato online: 17 mar 2025
Ricevuto: 04 nov 2024
Accettato: 02 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0225
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© 2025 Wenfei Zhao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Traction transformer T failure simulation 1
Traction transformer T failure simulation 1

Figure 2.

Traction transformer T failure simulation 2
Traction transformer T failure simulation 2

Figure 3.

Traction transformer T failure simulation 3
Traction transformer T failure simulation 3

Figure 4.

Suction current ratio method diagram
Suction current ratio method diagram

Figure 5.

Reactance ranging method diagram
Reactance ranging method diagram

Figure 6.

Traction power failure inference model
Traction power failure inference model

Figure 7.

Prediction results
Prediction results

Figure 8.

Diagnosis and prediction simulation for insulation fault of reactor front side grounding
Diagnosis and prediction simulation for insulation fault of reactor front side grounding

Error comparison analysis

Sample point Prediction value Real value Relative error (%)
1 2.0678 2.0837 0.76
2 1.0866 1.0907 0.38
3 2.0401 2.0661 1.26
4 1.5654 1.5152 3.31
5 1.2444 1.2174 2.22
6 1.9858 2.0198 1.68
7 2.6681 2.6494 0.71
8 1.1717 1.1586 1.13
9 2.9034 2.8231 2.84
10 1.6201 1.6435 1.42
11 2.7024 2.7277 0.93
12 2.1865 2.2072 0.94
13 2.1349 2.2009 3.00
14 1.2467 1.1942 4.40
15 2.9372 2.9752 1.28

Comparison of experiment results

Method Precision Recall F1 Accuracy
RF 99.52% 96.24% 98.83% 97.08%
Neural network 96.23% 99.96% 98.05% 96.43%
SVM 96.02% 99.96% 97.68% 95.77%
LSTM 96.02% 99.96% 97.68% 95.77%
Ours 99.87% 99.85% 99.76% 99.69%
Lingua:
Inglese
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Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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