Roller–rail parameters on the transverse vibration characteristics of super-high-speed elevators
Bibliographic record
Abstract
Interaction and wear between wheel and rail become increasingly serious with the increase in elevator speed and load. Uneven roller surface, eccentricity of rollers, and the looseness of rail brackets result in serious vibration problems of high-speed and super-high-speed elevators. Therefore, the forced vibration differential equation representing elevator guide rails is established based on Bernoulli–Euler theory, and the vibration equation of the elevator guide shoes and the car is constructed using the Darren Bell principle. Then, the coupled vibration model of guide rail, guide shoes, and car can be obtained using the relationship of force and relative displacement among these components. The roller–rail parameters are introduced into the established coupled vibration model using the model equivalent method. Then, the influence of roller–rail parameters on the horizontal vibration of super-high-speed elevator cars is investigated. Roller eccentricity and the vibration acceleration of the car present a linear correlation, with the amplitude of the car vibration acceleration increasing with the eccentricity of the roller. A nonlinear relationship exists between the surface roughness of the roller and the vibration acceleration of the car. Increased continuous loosening of the guide rail results in severe vibration of the car at the loose position of the support.
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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 | 0.000 |
| 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".