A Heavy-Haul Railway Corrugation Diagnosis Method Based on WPD-ASTFT and SVM
Bibliographic record
Abstract
Rail corrugation in heavy-haul railway increases the contact forces between the wheel and the rail and deteriorates the rail condition. Severe corrugation affects railway operational safety. Fast diagnosis techniques allow technical personnel to perform timely maintenance and repair, preventing the quick deterioration of rail corrugation. This paper presents a heavy-haul railway corrugation diagnosis method incorporating the time-frequency analysis with machine learning methods. First, the signal is decomposed into several subsignals by wavelet packet decomposition (WPD). The paper proposes an adaptive short-time Fourier transform (ASTFT) and performs the ASTFT on the subsignals to obtain the optimal resolution time-frequency distribution and compute the corresponding entropy. The dimensionality reduction based on mean entropy is then performed for the high-dimensional data. The training and testing samples are classified using Support Vector Machine (SVM). The adaptive short-time Fourier transform (ASTFT) is incorporated with the Renyi entropy and the particle swarm optimization algorithm, which achieves a better aggregation of the time-frequency distribution and reduces the computation cost. Finally, to assist the repair work and estimate the severity of the corrugation section, the corrugation index is proposed. The corrugation indices for the determined corrugation sections are calculated to measure the severity of the corrugation. Experimental studies performed on the axle-box vertical acceleration data collected from the heavy-haul comprehensive inspection train show that the method presented by this paper achieves higher accuracy when compared with conventional feature classification methods for time-frequency analysis. The accuracy of corrugation recognition for the presented method is 93%.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".