Prognostics Model to Predict Brake Rotor Thickness Variation
Bibliographic record
Abstract
Brake rotor thickness variation causes brake torque variation which can lead to brake judder and pulsation, steering wheel oscillations and chassis vibration. In this paper, we have proposed a prognostics methodology to predict the degradation level of brake rotor due to disc thickness variation. Leveraging the time and frequency domain analysis, this model creates health indicators to assess the health of the rotor and predict the rotor thickness variations of 36 micrometers or more. These health indicators that are calculated during braking events include: (i) envelope or variance of the brake master cylinder pressure (MCP); (ii) envelope or variance of the longitudinal acceleration (AX); (iii) the root mean square amplitude of the average order spectrum of the MCP at order one; and (iv) the root mean square amplitude of the average order spectrum of the AX at order one. This paper demonstrates that the above health indicators are significantly larger for a degraded brake rotor due to thickness variation compared to a healthy rotor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".