Bibliographic record
Abstract
Contaminants in engine and gearbox lubricants pose an ongoing maintenance challenge and can be indicators of a developing problem. Monitoring particles in engine and gearbox lubricants can provide diagnostic and prognostic information that could result in a reduction in maintenance cost and the prevention of additional damage. Current rotorcraft use magnetic chip detectors in order to monitor for potential component failure. With the advent of light weight, non-metallic components, such as ceramic bearings, the need arises to monitor the health of these components within the lubrication system. During the Future Advanced Rotorcraft Drive System (FARDS) program, the Aviation Development Directorate (ADD) - Aviation Applied Technology Directorate (AATD), Bell Helicopter, and Innovative Dynamics Inc. (IDI) developed and demonstrated a non-metallic debris monitoring technology using an off-the-shelf ultrasonic transducer. The sensor works by detecting metallic and non-metallic particles within an oil flow as it passes through the sensor's Field-Of-View (FOV). This paper recollects the design considerations, test approach, and lessons learned from testing a non-metallic debris sensor to a Technology Readiness Level (TRL) 6 in a gearbox test stand.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".