Fault Detection, Isolation and Prognosis for Control Moment Gyros Onboard Satellites
Bibliographic record
Abstract
Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.
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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".