Modeling Operational Risk to Improve Reliability of Unmanned Aerial Vehicles
Bibliographic record
Abstract
As Uncrewed Aerial Vehicle systems (UAVs) become more common and useful in public airspaces, this technology and its operation must be highly reliable to reduce risk to the general public. The objective of the present work is to improve the chances of mission success by analyzing and controlling the risk of UAV missions during different operational phases. Given the lack of reliability models for UAVs, we employ a systems reliability modeling methodology based on task decomposition and conditional risk analysis of each activity during a mission. The various risks involved in a specific mission activity are identified using Hazop techniques and Failure Modes and Effects Analysis (FMEA), along with the stopping conditions necessary to limit risks to an acceptable level. Different parts of a mission have different risk priorities, and the internal and external causes of failures of each activity are identified, described, and ranked according to their impact and uncertainties. This work constitutes the first phase of a broader research project. The risks of the UAV mission are modeled, after which it is verified by subject matter experts prior to implementing controls in an industrial case study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".