An Optimized Support Vector Regression for Identification of In-phase Faults in Control Moment Gyroscope Assembly
Bibliographic record
Abstract
One of the critical components in the satellite attitude control subsystem is the control moment gyroscope. If it fails, the satellite cannot finish its mission. Fault isolation followed by in-time corrective action can help prevent failures. However, it is necessary to know the fault severity for better maintenance planning and prioritize the corrective actions. This way, the more severe faults can be corrected first. Therefore, a data-driven fault identification scheme is proposed in this paper, adopting an optimized support vector regressor to determine the severity of multiple in-phase faults of the satellite control moment gyroscopes. The features are extracted using correlation analysis. A grid search is used to optimize the model's hyperparameters, and the R2-score is adopted to evaluate the model performance. It is shown that the proposed scheme can predict the fault severities with 94.9% accuracy, on average.
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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 | 0.000 | 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 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".