Estimation of Pressure Leakage Severity for Space Habitats Using Extended Kalman Filter
Bibliographic record
Abstract
Extraterrestrial habitat systems face numerous disruptions throughout their lifecycle, which may threaten crew safety. One serious risk is associated with air pressure leakage, which can occur due to sudden events such as micrometeorite strikes or airlock failures. To ensure proper functionality of the habitat, it is essential to detect the leak and assess its severity, quantified in terms of the leakage area, and communicate that information to the health management system in a meaningful way to support decision-making. A quantitative prediction of the remaining time before the detected fault results in system failure; its time-to-critical is also helpful for making decisions in such a resource-constrained environment. This paper presents a systematic study to develop and examine methods to estimate the current state of a pressurized vessel and predict the time-to-critical. To achieve this estimate in a real-time setting, an augmented extended Kalman filter method is used in conjunction with two noise identification procedures, namely, expectation maximization and variational Bayes. The robustness of the extended Kalman filter method, whether used independently or in conjunction with the expectation maximization or variational Bayesian approaches, is evaluated through a numerical study. The numerical study yields practical strategies for tuning the algorithm hyperparameters so as to obtain more rapid, accurate, and unbiased estimates of the fault intensity and the remaining time-to-critical. The performance and efficiency of these strategies are then experimentally validated by determining the leakage area and time-to-critical in a lab-scale pressure vessel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".