Verification and Validation of Hierarchical Fault Diagnosis in Satellites Formation Flight
Bibliographic record
Abstract
It is well known that for long-duration space missions, there is growing need for efficient utilization of telemetry data to enhance diagnostic performance and assist the less-experienced personnel in performing monitoring and diagnosis tasks. To address this need, we have, recently, developed a systematic and transparent fault diagnosis methodology within a hierarchical fault diagnosis framework for satellites formation flight. We developed our proposed hierarchical decomposition framework through a novel Bayesian network-based model, namely component dependence model (CDM). In this paper, we investigate the verification and validation of the CDM for fault diagnosis in satellites formation flight. We propose and develop a sensitivity analysis to verify the CDM by taking advantage of our systematic CDM development methodology. The proposed verification method satisfies the unique requirement of identifying CDM sensitivity when diagnostic performances of the algorithms that are deployed at one or more nodes of the CDM change. This implies that our verification approach and analysis are different from traditional sensitivity analysis that uses proportional scaling which is not applicable to the CDM methodology. Furthermore, in such analysis, a change in the model parameters under consideration is, typically, due to a change in the subjective judgment of an expert whose opinion is used in model development as opposed to the changes due to diagnostic performance variations. We demonstrate the proposed verification approach by using synthetic formation flight data, and show that our CDM development method does not lead to a fault diagnosis model that is sensitive to small variation in its parameters.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".