Hierarchical Fault Diagnosis in Satellites Formation Flight
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
Ground-support based satellite health monitoring and fault diagnosis practices involve around-the- clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multi-platform space missions due to the size of the telemetry data and an increasing need to make the long-duration missions cost- effective by limiting the operations team personnel. To utilize telemetry data efficiently, and to assist the less-experienced personnel in perform- ing monitoring and diagnosis tasks, we have developed a hierarchical fault diagnosis methodology. The hierarchical decomposition is presented through a novel Bayesian Network (BN) whose structure is developed from the knowledge of component health state dependencies, and the parameters are obtained by a proposed methodology that utilizes both node fault diagnosis performance data and domain experts’ beliefs. Our proposed model development procedure reduces the demand for expert’s time in eliciting probabilities significantly, and our approach provides the ground personnel with an ability to perform diagnostic reasoning across a number of subsystems and components coherently. Due to the unavailability of real formation flight data, we demonstrate the effectiveness of our proposed methodology by using synthetic data of a leader-follower formation flight configuration. Although our proposed approach is developed from the satellite fault diagnosis perspective, it is generic and is targeted towards other types of cooperative fleet vehicle diagnosis problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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