Monitoring a Hydraulically-Driven Feed Roll System with Sensors on aPrototype Pull-Type Forage Harvester
Bibliographic record
Abstract
Abstract. An experimental hydraulic drive was designed for a pull-type F41 Dion forage harvester to control and measure the rotational speed and applied load of feedrolls. Three types of sensors were placed on the experimental harvester: (1) four hydraulic pressure sensors to measure pressure in the input and output lines of the feedroll and header motors; (2) three integrated tachometers to measure motor speed, and (3) a potentiometer-based sensor to measure crop mass flow. Data was collected using a National Instruments USB 6216TM device and LabViewTM code. Pressure drop of the motor depended on mass flowrate of the crop material being conveyed. Power consumption increased with increasing rate of forage throughput. Increased forward speed decreased the specific energy requirements of the header and feedroll motors. The feedroll opening measurements using the potentiometer sensor were correlated with the experimental mass flowrate measured by weighing the forage wagon. Correlation coefficients were R2 = 0.97 and R2 = 0.59 at length of cut (LOC) of 15 and 9.5 mm, respectively. An analysis of variance indicated that both feedroll opening and throughput were affected by forward speed (p <0.01). These experimental results can allow for the optimization of the size of the drive components and facilitate the development of a throughput monitoring system.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".