Retrofitting Health and Usage Monitoring Systems (HUMS) for Unmanned Aerial Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the global drone market expected to reach USD 40.9 billion by 2027, increased system reliability has become critical not only to protect public safety and ensure mission success, but also to demonstrate risk controls as part of licensing. Health and Usage Monitoring Systems (HUMS) were primarily developed for real-time condition monitoring and machinery diagnostics of aircraft, naval vessels, and other civilian and military systems. HUMS on lightweight and low-cost Unpiloted Aerial Vehicles (drones) is a comparatively recent phenomenon. Incorporating existing HUMS used for other rotorcrafts directly into UAVs is very challenging, as the size, mass, and cost of such systems often do not match the capabilities of traditional drone structures, nor can they be easily integrated. Several health monitoring technologies geared specifically for UAVs have been developed, including Fiber Bragg Grating (FBG)-based strain and temperature sensors, Piezoelectric (PZT) sensors, and ultrasonic propagation imaging sensors, among others. This paper discusses and evaluates the recent research on five classes of health and usage monitoring systems for UAVs currently in use, namely for structural, electrical, temperature-related, vibration-related, and environment-related failure modes. We then develop some general requirements for a HUMS prototype system that is easily retrofittable with a broad range of small to midsize UAVs, investigate which of the sensor systems may be most suitable for the prototype HUMS, make a comparative analysis of these systems, and identify their limitations. Lastly, we present work in progress on System Architecture options for integrating the different classes of sensors into a single, comprehensive HUMS.
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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 | 0.001 | 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 it