Integrated Health Monitoring for Multiple Air Vehicles
Bibliographic record
Abstract
Abstract This chapter proposes an integrated health monitoring system whose objective is to maintain the integrity of a fleet of small unmanned aerial vehicles, or UAVs, despite the occurrence of actuator faults and body damage. The monitoring system relies on distributed, team‐level abrupt and nonabrupt fault detectors (NaFDs) and enables UAV teams to compensate for an ineffective individual vehicle fault detection and recovery system, if any. The abrupt and nonabrupt fault detectors utilize relative positions and speeds of vehicles within sensor range, in addition to exploiting information available through the communication network. A special attention is given to the selection of the detector thresholds, at the design phase, to minimize the average time of detection and the probability of missed detections, while constraining the probability of false alarms to an acceptable limit. For such purpose, an automated procedure based on stochastic search is proposed. Integrated health monitoring and control is demonstrated numerically by means of high‐fidelity, nonlinear 6‐degree‐of‐freedom (6‐DOF) simulations of teaming small‐scale UAVs. The chapter shows that concurrent nonabrupt and abrupt faults can be detected and effectively compensated for, thus preserving the integrity of a formation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".