Development of a Self-Powered Speed Sensor for Drilling Tools with Multiple Friction Belts
Bibliographic record
Abstract
Drilling is a technique that uses drilling tools to drill holes from the surface to the underground, and it is an important means of mining solid minerals and oil and gas resources. Whether it is a screw drill or a turbine drill, the rotational speed is key data to determine the operating status of the drilling tool and the downhole conditions, so it is necessary to measure the rotational speed of the drilling tool in real-time. In this paper, a multi-friction belt drill speed sensor based on a triboelectric nanogenerator was developed. Its design is a parallel structure of multiple friction belts, and any one of the friction belts can independently measure the speed and generate electricity, thus having high power generation performance. Through the sensor performance test, it is found that the sensitivity of the speed sensor is 0.0167 Hz/rpm, the linearity is 2.7%, and the maximum relative error is 3.6%. By analyzing the output relationship of the sensor under different friction belts, it was found that the maximum output voltage of the speed sensor in a single friction belt structure is 18 V, the maximum output current is 1.5 uA, and the maximum output power of 12 uW is obtained when connected to an external$\mathbf{2.8}\times \mathbf{10}^{\mathbf{7}}\mathbf{\Omega}$. The output voltage of the multi-friction belt (n) drill speed sensor is consistent with that of the friction belt structure, while the output current and power are$\mathbf{n}$times that of the single friction belt structure. The sensor can maintain a high voltage output even in high-temperature and high-pressure environments, which can meet the needs of deep well speed monitoring.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".