Two-Dimensional Optimization Framework of Online Interpretable Time-Frequency Feature Learning for Practical Machine Health Monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data-driven feature extraction for machine health monitoring has garnered significant attention, yet two key limitations remain unaddressed: lack of interpretability and the need for extensive historical fault data. To overcome these problems, an online two-dimensional optimization framework is proposed that enables interpretable time-frequency feature extraction and health index (HI) construction without requiring faulty samples for model training. Our approach introduces a convex hull-based closest point optimization model for estimating time-frequency instances and learning interpretable time-frequency features. By leveraging a small set of baseline vibration samples and recent online data, rapid fault diagnosis can be achieved based on optimized interpretable time-frequency features. This method also facilitates long-term degradation tracking by constructing and updating an HI from collected time-frequency spectrograms. Once machine faults appear, updated time-frequency features can show apparent and interpretable fault signatures for prompt fault alarming. Moreover, the proposed framework allows continuous HI updates for incipient fault detection and degradation tracking. The proposed framework is validated by using two run-to-failure datasets and ablation experiments are conducted to demonstrate its superiority.
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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 | 0.001 |
| 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 it