Self‐Powered In Situ Sensing for Planetary Gearbox via Floating Freestanding‐Layer Mode Triboelectric Nanogenerator
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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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 | 0.000 |
| 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) | 0.001 | 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 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".