Uncertainty-aware few-shot gearbox fault diagnosis via multi-scale spatial-attention and transformer integrated evidential deep learning
Bibliographic record
Abstract
Abstract To address the challenges of data scarcity and unreliable prediction confidence in wind turbine gearbox fault diagnosis, an uncertainty-aware diagnostic and intelligent early-warning framework is proposed, integrating dynamic data augmentation with an improved evidential deep learning (EDL) framework. The proposed framework adaptively combines CutMix and Mixup strategies to effectively enrich training samples and alleviate overfitting in extremely few-shot scenarios. A multi-branch, multi-scale convolutional network with coordinate attention and a Transformer Encoder is designed to jointly capture robust local and global representations. Based on EDL, the model achieves both high diagnostic accuracy and reliable uncertainty quantification. Furthermore, the quantified uncertainty is visualized through a three-level control-chart-based early warning mechanism, enabling proactive and hierarchical fault alerts. Experimental results show that the proposed method maintains high accuracy, provides trustworthy uncertainty estimation, and supports dynamic early-warning support in few-shot conditions of wind turbine gearbox, showing strong potential for practical industrial deployment.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".