Anomaly score for rotational machines using conditional Variational Auto-encoder adaptable to speed changes
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
The basic way for detecting anomalies in rotating equipment would be to use the frequency spectrum of vibration. These days, however, rotating devices are generally driven by inverters, and changes in the rotational speed cause significant changes in the vibration spectrum. In order to detect anomalies regardless of dependence on operating conditions, AI technology that performs learning to capture normal conditions is useful. As a specific method, Conditional Variational Auto-encoder (CVAE), which takes rotational speed information as conditional inputs, is promising. We are trying CVAE using publicly available dataset provided by the University of Ottawa to search for optimal conditions. We report methods used and results obtained, including limiting training samples, increasing the number of conditions, interpolation and extrapolation of condition inputs.
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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 | 0.001 | 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