Running Status Diagnosis of S700K Turnout Based on VMD-KPCA and Fuzzy Clustering
Bibliographic record
Abstract
S700K turnout is the key equipment of railway line conversion. The diagnosis of S700K turnout in a normal, sub-health, and fault running state is the primary premise to ensure the safe operation of the railway. Aiming at the consistency between the characteristics of the power curve of S700K turnout and its state information, this paper proposes a new algorithm based on variational mode decomposition (VMD) and kernel principal component analysis (KPCA) to extract the characteristics of the power curve of S700K turnout. It uses fuzzy clustering analysis to diagnose the running state of S700K turnout. First, to extract the detailed components of the action power curve, it is decomposed into intrinsic mode function with limited bandwidth (BIMF) by VMD. Secondly, the multi-scale permutation entropy (MPE) is used to characterize the signal complexity of the power curve and different BIMF components, which are taken as the running state feature set. After KPCA analysis, eigenvalues with a contribution rate greater than 95% are selected as the state eigenvector. The experimental results show that the diagnosis algorithm can effectively identify the running state of S700K turnout, meet the characteristics of fewer fault samples of S700K turnout, and do not need to train in advance, which is of great significance for field guidance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".