Spacecraft 3-axis Controlled Attitude Determination and Control System Reaction Wheels Fault Detection, Isolation and Identification using Machine Learning Techniques
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
Spacecraft attitude control systems rely on reaction wheels as the primary means of precise three-axis attitude control. Faults in these reaction wheels might lead to system instability and, in severe cases, mission failure. This paper presents advanced machine learning-based techniques for the detection, isolation, and identification of reaction wheel faults in spacecraft.The proposed approach leverages advanced data analytics and machine learning algorithms to analyze sensor data from the reaction wheels, enabling early detection of faults and effective isolation of the faulty component and identify the types of faults detected, specifically, voltage, current and temperature faults.Three-axis controlled satellite high-fidelity models are simulated to generate data for both nominal and faulty states of RW. The simulated data is employed with the FDII approach. The generated data is passed into five different machine learning classifiers, the isolation and identification results are verified via cross-validation. The proposed techniques is tested on three defined datasets using the three-orthogonal RW configuration to verify its robustness. The results show that the system has higher isolation and identification accuracy when compared to other studies that used various methodologies.
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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