Open Access
Review
Table 2
Engineering applicability analysis of traditional machine learning methods in nuclear power pump fault diagnosis.
| Method | Highest accuracy (%) | Real-time performance (ms) | Minimum sample requirement | Applicable scenarios |
|---|---|---|---|---|
| SVM | 92.0 | 12.3 | 150 | Stable operating condition classification [7] |
| WPT-MSVM | 96.8 | 46.7 | 300 | Multi-fault concurrency [15] |
| OCSVM | 87.3 | 8.2 | 50 | Early warning under label scarcity [17] |
| STFT-KNN | 89.4 | 22.1 | 100 | Steady-state vibration analysis [5] |
| GEP | 91.2 | 35.8 | 200 | Strong nonlinear interference [10] |
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.