Bibliographic record
Abstract
Self-lubricating rotor blade pitch control bearings are critical to safe operation and fleet readiness. However, detecting bearing liner wear can be subjective and time consuming. Standard measurement techniques could lead to unsafe operational conditions or poor bearing life utilization. Excessive inspections, unplanned downtime and poor part utilization may be driven for safety and result in increased cost. New Hampshire Ball Bearings (NHBB) has developed a novel new approach that provides clear indication of when a bearing should be replaced. An embedded bearing liner wear sensor is connected to an Ultra High Frequency (UHF) passive Radio Frequency Identification (RFID) communication device to communicate bearing status to a hand held reader. Status of each individual bearing is reported. This new technology will allow operators and maintainers to conduct bearing maintenance when it is required by actual bearing condition instead of a fixed schedule based on Condition Based Maintenance (CBM) systems. Application specific and component functional testing on the wear sensor system (conducted at NHBB) shows wear performance similar to traditional blade pitch control applications with no impact to overall system operational capabilities. NHBB and Bell Helicopter are collaborating to introduce this technology on the Bell 525 with flight testing tentatively planned for 2017. Future work on this project will focus on aircraft and maintenance system integration.
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 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) | 0.000 | 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 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".