Fault prediction and maintenance of urban rail transit power supply system based on big data
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17 mar 2025
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Publicado en línea: 17 mar 2025
Recibido: 04 nov 2024
Aceptado: 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.
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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% |