Systematic Health Monitoring and Management for UAV Hydrogen Energy Systems via a Hybrid Excitation Operator
Bibliographic record
Abstract
The hydrogen-powered unmanned aerial vehicle (UAV) is a pivotal part of zero-carbon aviation, yet the online health monitoring and management of fuel cells (FCs) during flight remain a predominant challenge. In response to the critical concern, this article initiates an online systematic approach of health monitoring and management (SAHMM) for hydrogen fuel cells in aerial applications consisting of four stages. First, a voltage-based adaptive health monitor continuously monitors the state of health of the fuel cell, detecting faults in real time. Second, to classify the faults, a hybrid excitation operator (HEO) including a converter regulation signal and an unmanned aerial vehicle maneuver signal is injected to extract fault features when a fault is detected. The HEO, as a control-for-diagnosis design, exploits unmanned aerial vehicle maneuvers for fault feature extraction while maintaining position tracking performance, relieving the burden of the energy subsystem during excitation. A fuzzy logic classifier then categorizes the detected fault. Finally, recovery measures according to the fault type will be adopted to restore system health. The proposed SAHMM is the first to tackle the health monitoring and recovery of aviation hydrogen fuel cell systems in a systematic approach. The effectiveness of SAHMM is validated through real flights and ground experiments, where online fault detection and 97.22% fault classifier accuracy are demonstrated, and successful health recovery is achieved based on the diagnosis result. Moreover, the monitor reduces false detection rate by integrating online model estimation to track the fuel cell performance drifts, and a more than 37% reduction on energy subsystem stress is observed thanks to the HEO design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".