Integrated telemetry analysis using human expert knowledge and the logical analysis of data
Bibliographic record
Abstract
Telemetry data received from satellites during in-orbit operation are typically analyzed by experts to identify faults that might lead to potential subsystem failures. A typical method is to apply limit-checking procedure to locate off-nominal features which do not fall within the expected normal range of values. Human-experts' analysis is usually limited. It focuses on analyzing selected group of features that would interpret a specific situation that might be taking place onboard the satellite. When the size of telemetry features and number of observations are in the order of hundreds or thousands, a full expert-based analysis is almost impossible to achieve. In this paper, expert-based analysis is leveraged by the automation of the telemetry analysis process based on a machine learning technique called the Logical Analysis of Data (LAD). LAD is a pattern recognition and classification approach that combines ideas and concepts from optimization, combinatorics and Boolean functions. One of the main advantages of LAD is its explanatory power, which offers a classification and an interpretation of the root causes of the events under study. Patterns generated via LAD are easily understood by experts as they are constructed from the features within the set of observations. Consequently, LAD is used in numerous practical applications. We are applying LAD in the process of fault diagnosis and prognosis in satellites by combining the domain expert's knowledge and the knowledge extracted by LAD. The procedure begins by performing a fault tree analysis (FTA) and a list of corrective actions, by domain human experts. This analysis is usually limited by the expert's knowledge to represent known faulty states of the system. However, operation data collected over a period of time can introduce meaningful hidden knowledge about faulty states that were not represented in the original FTA. We apply LAD to find this hidden knowledge in data collected through simulation, testing and in-flight operation. The results reflect significant interpretation power and an effective leverage of the obtained knowledge by the integrated telemetry analysis tool when compared to traditional limit-checking method.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".