Bibliographic record
Abstract
Fault-tolerance in commercial aircraft applications is typically achieved by redundancy. In such redundant systems the primary component is checked before the start of a flight to see if it operates correctly. The aircraft will not take off unless the primary is functioning. Airplane manufacturers must certify the airplane systems to be safe for flight. One means of safety certification is by safety analysis which shows that the probability of failure in a typical flight is bounded. The probability bound requirement for a system is based on the criticality of system failure. Usually backup components are checked at intervals that span multiple flights. The first backup may be checked more frequently than the second or higher levels. This leads to flights where the system may have latent faults in the backup components. The probability of failure in such cases varies from flight to flight due to the different exposure times for components in the system. So we are led into the concept of “Average Probability of Failure per Flight”. AC/AMC 25.1309 and SAE ARP 4761 document simple formulas for average probability failure calculation which are applicable under restricted conditions such as small failure rates etc. In this paper we will discuss new methodologies and equations which relax these restrictions. The intent of this paper is to provide a single source for Average Probability of Failure per Flight calculations, describe the role of advanced Markov and Petri Net tools in these calculations, and provide best practice recommendations.
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.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".