Data-Driven Designs of Fault Identification via Collaborative Deep Learning for Traction Systems in High-Speed Trains
Bibliographic record
Abstract
Due to the advanced development of sensor technology, the data deluge has begun in the complex systems of high-speed trains (HSTs) and, therefore, hastens the popularity of data-driven research. Among these activities, data-driven detection and identification of faults have received considerable attention to ensure the safe and reliable operations of HST, especially the deep learning-based methods. Up to now, these deep learning-based methods are effective only for static systems. It, hence, motivates us to develop the data-driven fault identification (FI) method for traction systems in HST. In this study, we will develop an FI method via the collaborative deep learning method, where the first neural network is used for eliminating dynamic behaviors, and the second neural network is responsible for identifying the fault amplitude. By the use of the proposed neural networks with a deep architecture, the FI task can be achieved in a collaborative fashion. Its successful application on the traction systems of HST illustrates the effectiveness of collaborative deep learning on the one hand and opens an avenue on the data-driven FI methods using neural networks on the other hand.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".