A fault diagnosis method for rotating machinery in nuclear power plants based on long short-term memory and temporal convolutional networks
Why this work is in the frame
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Bibliographic record
Abstract
Vibration signals typically used for health monitoring of rotating machinery has highly integrated spatio-temporal correlations. However existing studies rarely explore the impact of spatial correlation features of rotating machinery internal components on their vibration signals. To identify the health condition of rotating machinery in NPPs in terms of spatial–temporal correlation, we propose a fault diagnosis method with combination of the long short-term memory and temporal convolutional networks. The spatial and temporal features in the vibration signals of rotating machinery are extracted using the two networks and then fused to diagnose its faults. The model was assessed against the Case Western Reserve University bearing dataset, University of Ottawa bearing dataset and Southeast University gearbox dataset. The results show that its diagnostic accuracy reaches up to 99.56 %, 100 %, and 100 % on the three datasets, respectively, and outperforms other five well-designed comparative models, demonstrating its effectiveness and superiority in rotating machinery fault diagnosis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it