Performing transient vibroacoustic analysis on a continuously variabletransmission using a Vold-Kalman filter
Bibliographic record
Abstract
A continuously variable transmission (CVT) is a type of automatic transmission system that uses belts and pulleys to provide an infinite number of gear ratios. The system consists of two pulleys: a primary drive pulley and a secondary drive pulley, each with a fixed and a moving sheave. In snowmobiles, he primary pulley is connected to the crankshaft of a two-stroke engine, while the secondary pulley is connected to the track element. The use of a two-stroke combustion engine can cause torque fluctuations to propagate through the crankshaft to the CVT, resulting in excessive vibration and noise levels. To evaluate the vibroacoustic emissions of CVTs, alternative methods have been developed due to the limitations of road noise testing. One such method is to use an adaptive controller on a dynamometer to perform a repeatable acceleration phase, allowing for efficient and sophisticated acoustic analysis. Vold-Kalman order tracking (VKF-OT) with a phase-locked loop (PLL) is used to analyze the non-stationary periodic components of the noise generated by the CVT. This study conducted dynamometer experiments to identify the noise generated by the CVT during the acceleration phase. However, the analysis of the acoustic data can be difficult due to the lack of repeatability of the speed ramps during dynamometer tests. To solve this problem, post-processing methods have been proposed to synchronize the acoustic measurements taken at different speed ramps. The objective is to develop an adaptive approach to identify the prominent noise order, allowing a comparison of vibroacoustic performance between different CVT designs. Experimental results demonstrated the effectiveness of this experimental tool to analyze the attenuation of certain low order intensities during the acceleration phase.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".