Hierarchical fault diagnosis and health monitoring in multi-platform space systems
Bibliographic record
Abstract
Current spacecraft health monitoring and fault diagnosis practices that involve around-the-clock limit-checking and trend analysis on large amount of telemetry data, do not scale well for future multi-platform space missions due to the presence of larger amount of telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team. The need for efficient utilization of telemetry data by employing machine learning and rule-based reasoning has been pointed out in the literature in order to enhance diagnostic performance and assist the less-experienced personnel in performing monitoring and diagnosis tasks. In this research we develop a systematic and transparent fault diagnosis methodology within a hierarchical fault diagnosis framework for multiplatform space systems. Our proposed Bayesian network-based hierarchical fault diagnosis methodology allows fuzzy rule-based reasoning at different components in the hierarchy. 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. Our proposed methodology is likely to enhance the level of autonomy in ground support based spacecraft health monitoring and fault diagnosis.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".