A New Real-Time Automated Ground Health Monitoring System at a Satellite Ground Control Station
Bibliographic record
Abstract
A new real-time detection/diagnosis methodology for an Automated Ground Health Monitoring System (AGHMS) is applied at a satellite ground control station. The technological innovations in this research are focused on the identification of abnormal transient response profiles from a satellite 6-DOF attitude control platform. The identification will be made by comparing, in real time, the filtered (Kalman) measurements to a synchronized model reference system. The methodology used to accomplish this task will be software intensive and perfectly compatible with the open physical architecture of existing monitoring devices and their automated control system mechanisms. The innovations demonstrated will be (1) a real-time Extended Kaiman filter to eliminate the measurement noise; (2) the formulation and use of a dynamic threshold detection system to identify abnormal state estimates as well as covariance estimates; and (3) the generation of an intelligent fault-mode file with corrective control commands to stabilize mild detected faults. The objective of this article is to provide these technical enhancements by handling and evaluating test data differently. An example is included to demonstrate these technical innovations. The AGHMS methodology will demonstrate real-time signal detection using an Extended Kalman filter (EKF) to obtain the best estimate of measurements (i.e., the dynamic parameters of a system); to obtain precise knowledge of the attitude of a satellite or a spacecraft that has an onboard magnetometer for attitude measurements; to study and analyze the state co-variance, as well as the error co-variance of the system; and to enhance the processing capability by monitoring systems in real time, perform systems detection/diagnosis, and actively control the environment/process based on these onboard sensor readings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".