FTIR Condition Monitoring of In-Service Lubricants: Ongoing Developments and Future Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. KEY WORDS: Condition MonitoringFTIR SpectroscopySpectral ReconstitutionASTMAutomated AnalysisLubricant Analysis Review led by Cyril Migdal Notes A Reporting values in absorbance/0.1 mm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it