A sparse FP-VAR model for representing multichannel non-stationary baseline vibration signals from a gearbox
Bibliographic record
Abstract
Time series model-based approaches are widely employed for condition monitoring of gearboxes. These approaches rely on accurate time series modeling of the baseline vibration signals. This paper proposes a sparse functional pooled-vector auto-regression (FP-VAR) model for representing multichannel non-stationary baseline vibration signals from a gearbox. A model-based fault detection scheme is also presented. The proposed sparse FP-VAR model combines the advantages of the sparse time series modeling and utilization of multichannel vibration signals, where the former has shown to have higher modeling accuracy than conventional non-sparse time series modeling, and the latter enables the removal of the correlated random noise between channels. Simulation results have demonstrated that the sparse FP-VAR model has higher modeling accuracy than the sparse FP-AR and conventional non-sparse FP-VAR models. Subsequently, the model-based fault detection scheme has a higher fault detection rate than using the other two models.
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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 | 0.001 | 0.001 |
| 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 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".