A New Method for Monitoring Gears Surface Failures Using Enhanced Image Registration Approach
Bibliographic record
Abstract
In this paper, we present an image registration approach to cope with inter-image illumination changes of arbitrary shape in order to monitor the development of micro-pitting in transmission gears. Traditional image registration approaches do not typically account for inter-image illumination variations that negatively affect the geometric registration precision. Given a set of captured images of gear surface degradation with different exposure times and geometric deformations, the correlation between the resulting aligned images is compared to a reference one. The presented image registration approach is used with an online health monitoring system involving the analysis of vibration, acoustic emission and oil debris to follow the development of micro-pitting in transmission gears. The proposed monitoring system achieves more registration precision compared to competing systems. This paper experimentally validates the system's capabilities to detect early gear defects and reliably identify the gradual development of micro-pitting in gears, so that it could be used in predictive health monitoring (PHM) systems and overcome the disadvantages of the most commonly used methods, such as gear flank profile scanning, replica sample analysis and conventional image analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.002 |
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".