Self‐Powered In Situ Sensing for Planetary Gearbox via Floating Freestanding‐Layer Mode Triboelectric Nanogenerator
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Planetary gearboxes, a critical component in industrial transmission systems, present significant challenges for condition‐monitoring technologies owing to their complex motion characteristics. Traditional monitoring methods are often susceptible to environmental noise interference and rely on external power supply systems, complicating maintenance and increasing costs. This study presents an in situ sensing system for planetary gearboxes using a floating freestanding‐layer‐mode triboelectric nanogenerator (FF‐TENG) integrated on the side of planet gear. By utilizing the inherent axial micromotion characteristics during operation, the system employs a floating‐electrode structure with adaptive gap adjustment to prevent contact wear between the electrode and the dielectric layer, which significantly enhances system durability. Key parameters are systematically analyzed to examine the FF‐TENG's output characteristics and working mechanism. The FF‐TENG exhibited outstanding speed‐monitoring capabilities across diverse rotational speeds. Furthermore, a local maximum mean discrepancy improved transformer encoder model is designed. The model achieved 98.4% accuracy in fault diagnosis across different rotational speeds and fault modes. Then, FF‐TENG is applied to the planetary gearbox of a robotic arm, realizing in situ sensing of its motion behavior. This research introduces a self‐powered in situ sensing system for planetary gearboxes using TENG, providing a new approach for rotating machinery in situ sensing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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