Health index extraction for power-shift steering transmission using selected oil field data
Bibliographic record
Abstract
The concentration of wear debris in used lubricating oil is generally observed from spectral oil analysis. The element concentration data are one of the commonly used oil field data for degradation evaluation of power-shift steering transmission. However, in practical applications, the underlying degradation degree of the power-shift steering transmission is difficult to evaluate due to the complexity of element concentration data. To solve this problem, we propose a health index extraction methodology using a weighted average method to better characterize the underlying degradation, which leads to an accurate estimation of the residual life before the power-shift steering transmission needs to be maintained. The extracted health index not only maximizes the monotonic trend of underlying degradation but also minimizes the failure threshold variance between different power-shift steering transmissions. The method includes element concentration data modification, data selection, and data fusion steps that result in a reasonable power-shift steering transmission degradation model. Finally, a case study is provided to illustrate the proposed method. The results show that the extracted health index outperforms each selected element concentration data.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".