FTIR Condition Monitoring of In-Service Lubricants: Ongoing Developments and Future Perspectives
Bibliographic record
Abstract
Condition monitoring of used lubricants by Fourier transform infrared (FTIR) spectroscopy is reviewed and placed in context of the recently approved ASTM Practice E 2412-04 developed by the Joint Oil Analysis Program (JOAP) as a standardized means of trending oil/lubricant condition. A new spectral reconstitution procedure is presented and evaluated as an alternative means of executing this ASTM practice, its objective being to minimize sample handling issues associated with the high viscosity of most in-service oils. Used diesel crankcase oils were analyzed in both their neat and diluted forms in 100 and 200 μ m KCl cells, respectively, and the coefficient of variation (CV) for accuracy of the spectral reconstitution procedure was < 5% for all the parameters evaluated. Spectral reconstitution simplifies and facilitates sample handling, avoiding the need for peristaltic or syringe pumps and allowing up to 120 samples/h to be analyzed. The need for a solvent rinse between samples is also avoided, and cell clogging and tubing wear are effectively eliminated. The spectral reconstitution technique also makes the ASTM practice compatible with newer FTIR systems which are capable of quantitative determination of AN, BN, and moisture.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".