Monitoring Carbon Slide Wear in Metro Train Current Collector Shoes Using 3-D Point Cloud Data
Bibliographic record
Abstract
The carbon strip on the current collector shoe of subway trains is a key component in the traction power system. Its wear directly affects the safety and reliability of train operation. Traditional manual inspection methods are inefficient and prone to human error. Existing image-based inspection methods also struggle with detecting complex shapes, edge wear, and more importantly, lacking geometrical information. To solve these problems, this paper develops a non-contact, high-precision monitoring system based on 3D point clouds and presents how 3D point cloud data is processed to give both thickness and spalling. Using line laser scanning, the system captures dense 3D data of the current collector. A nearest neighbor search and weighted extremum extraction are used to find the top and bottom edges of the carbon strip and calculate thickness. A robust plane fitting method based on Huber loss is applied to estimate the area of spalling. Experiments show that this method achieves sub-millimeter accuracy (±0.7mm) in thickness measurement, improves efficiency by about 20 times compared with manual inspection, and can automatically identify worn edges, providing reliable data for predictive maintenance.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".