Empirical Methods for Reaction Wheel Micro-Vibration Verification in a Production Environment
Bibliographic record
Abstract
There is great interest in high precision attitude control of satellites, in particular for missions that operate payloads with stringent pointing requirements. Reaction Wheels (RW) are an integral part of a satellite's Attitude Determination and Control System (ADCS). However, a drawback of using RWs is that due to imperfections such as rotor imbalance and bearing defects, RWs are a source of micro-vibration. These phenomena can lead to internal disturbances which in turn may lead to degraded mission performance. Quantifying the micro-vibration generated by RWs is a critical and time intensive process. A two-step process to verify and characterize RW micro-vibration performance will be presented in this paper. The verification is performed by conducting a short-form test in a single axis using a Laser Doppler Vibrometer (LDV). The characterization is performed by conducting a long-form test in all axes using a Multicomponent Force Dynamometer. Both tests allow for the determination of a RW’s imbalance and noise profile which can be evaluated against a pass/ fail criteria. A challenge associated with scaling production is verifying wheel micro-vibration performance in an efficient manner, while maintaining a high degree of product assurance. Quality control limits and correlation analyses were conducted to aid in developing a more efficient process for RW verification and characterization. A framework for the refined two-step process will be presented alongside a case study to identify acceptable micro-vibration performance, using Sinclair Interplanetary’s RW-0.06 product. The methods presented here can be used within the small satellite community to better understand and predict the micro-vibration performance of reaction wheels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".