Dynamic characteristics of electromechanical coupling of body-suspended drive system for high-speed trains under wheel polygonal wear
Bibliographic record
Abstract
Wheel polygonal wear can trigger intense wheel–rail interactions in railway vehicles, posing a significant threat to traffic safety. However, the mechanism and interaction of the influence of wheel polygonal wear on the electromechanical coupling characteristics of high-speed trains remain unclear. Therefore, this paper proposes a joint simulation modelling method that takes into account the integration of the electrical subsystem with the complete mechanical subsystem. A comprehensive analysis is conducted on the dynamic response of the high-speed train’s body-suspended drive system under a wide range of frequency excitations arising from harmonic torque, gear meshing, and wheel polygon wear. The results indicate that the electrical system generates a substantial amount of harmonic frequency components, resulting in a significant increase in the vibration of the train. The wheel polygon wear causes a broadening of the low-frequency resonance band, revealing a relatively prominent observation interval for the dynamic characteristics. Concurrently, it is discovered that the 23rd-order wheel polygon wear excites the system’s resonance frequency, and as the wavelength of the wheel polygon increases, the acceleration vibrations intensify progressively. Research reveals first the interaction of electrical system and mechanical system under wheel polygon wear, especially in the resonance gain characteristics under polygons and harmonic torques.
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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".