Method to Detect and Isolate Brake Rotor Thickness Variation and Corrosion
Bibliographic record
Abstract
Brake rotors are essential parts of the disc brake systems. Brake rotor thickness variation (RTV) and corrosion are among top failure modes for brake rotors, which may lead to brake judder and pulsation, steering wheel oscillations and chassis vibration. To improve customer satisfaction, vehicle serviceability and availability, it is necessary to develop an onboard fault detection and isolation solution. This study presents a methodology to monitor the state-of-health of brake rotor system to reduce costs associated with scheduled inspection for autonomous fleet or corrective maintenance. We converted the vehicle signals from time-domain to angle-domain and determined health indicators to estimate the RTV level of the rotors. Variance, envelope and order analysis of the brake circuit pressure, longitudinal acceleration and wheel speed sensor signals in angle-domain were promising health indicators to differentiate healthy and faulty rotors. A classification model was developed to fuse the health indicators and estimate the state-of-health of the rotors to report the most degraded rotor with corner isolation. Results showed that using this concept we were able to detect failure levels of 20 microns and larger and meet the customer requirement. Robustness analysis showed that the concept is robust to the noise factors of tire type, tire pressure and vehicle weight. The sensitivity analysis showed that the algorithm is sensitive to two of the calibration parameters (i.e., brake pedal position gradient (BPPG) threshold and the filter order used to derive BPPG) used to determine the brake event and enable the algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".