Open Access
Review
Table 8
Unified benchmark comparison of representative soft sensing models for pump fault diagnosis.
| Model category | Accuracy (%) | Robustness drop (%) | FLOPs (106) | Model size | Sample requirement |
|---|---|---|---|---|---|
| SVM (RBF) | 85–92 | 12.3 | N/A | Small | Low (≤1,000) [7] |
| CNN (1D) | 93.5 | 7.8 | 12.4 | Medium | High (3,000–5,000) [27] |
| ST-CNN (Hybrid CNN–LSTM) | 95.1 | 6.2 | 18.7 | Medium | High (3,000–5,000) [16] |
| Transfer learning (ResNet-based) | 97.3 | 4.9 | 24.5 | Large | Medium (1,000–3,000) [18] |
| AFARN | 98.0 | 3.1 | 20.8 | Large | High (3,000–5,000) [12] |
| SCSAN | 99.1 | 1.8 | 30.2 | Large | High (3,000–5,000) [23] |
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