Very High Frequency Monitoring System for Engine Gearbox and Generator Health Management
Bibliographic record
Abstract
In cooperation with the major propulsion engine manufacturers, the authors are developing and demonstrating a unique very high frequency (VHF) vibration monitoring system that integrates various vibro-acoustic data with intelligent feature extraction and fault isolation algorithms to effectively assess engine gearbox and generator health. The system is capable of reporting on the early detection and progression of faults by utilizing piezoelectric, optical, and acoustic frequency measurements for improved, incipient anomaly detection. These gas turbine engine vibration monitoring technologies will address existing operation and maintenance goals for current military system and prognostics health management algorithms for advanced engines. These system features will be integrated in a state-of-the-art vibration monitoring system that will not only identify faults more confidently and at an earlier stage, but also enable the prediction of the time-to-failure or a degraded condition worthy of maintenance action. The authors have made significant progress toward identifying, computing, and comparing the high frequency feature sets generated with various vibro-acoustic measurement techniques. Specifically, the technology has been demonstrated on two subscale test stands. The first is a generator test rig that was equipped with a laser vibrometer and two high-frequency accelerometers. Various mechanical and electrical faults were seeded, with an emphasis on generator bearing faults. Initial results show very good detection capability in frequency bands well above those used in traditional vibration analysis. Another focus, accessory gearbox systems, was addressed for feasibility through a gearbox test rig, which was instrumented with high bandwidth accelerometers and wideband and narrowband acoustic emissions (AE) sensors. Baseline, seeded fault, and fault progression tests were conducted, including tests with various levels of gear tooth corrosion. Successful detection of this fault was then demonstrated using a number of new, innovative approaches. A statistical analysis was also performed to compare the approaches, with narrowband acoustic emission and high frequency vibration features performing the best.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".