Electromagnetic Induction Sensor Design for Metal Oil Wear Particle Detection
Bibliographic record
Abstract
When the mechanical equipment is working, the internal units interact with each other to produce different degrees of wear, wear will produce wear particles, the generated wear particles will flow around with the lubricating oil, if not timely detection and maintenance, it will aggravate the wear process, thus affecting the service life of the entire mechanical system. In this paper, a new high-precision three-coil electromagnetic induction sensor for metal oil wear in mechanical equipment is proposed, and Laval nozzle is innovarily applied to the oil inlet of the sensor. Firstly, based on the principle of electromagnetic induction, the physical characteristics of metal particles and their influence on magnetic field are analyzed, and the working principle and key parameters of the sensor are determined. Secondly, the overall structure of the sensor is designed, and the packaging and protection design are carried out according to the working condition. Finally, the coil schematic was established in Maxwell and boundary conditions and initial conditions consistent with the actual working conditions were set up for simulation verification under different types of wear particles at 1m/s. The results showed that the sensor's signal response was obvious under different particle sizes, which verified the rationality of the sensor design.
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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 | 0.000 | 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".