An Overview of Progress and New Developments in FTIR Lubricant Condition Monitoring Methodology
Bibliographic record
Abstract
Abstract Fourier transform infrared (FTIR) spectroscopy has found an important analytical niche in the field of condition monitoring (CM) of lubricants, lubricant quality being a primary determinant of the wear and operational efficiency of engines and machinery. The ability to track a wide range of functional groups associated with chemical changes in lubricants by FTIR spectroscopy allows the overall quality of lubricants to be assessed and informed decisions made as to whether a lubricant needs to be replaced or not. Newer automated FTIR systems in centralized laboratories are now capable of analyzing oil samples at rates of>120 samples/h, providing trending information on soot, moisture, glycol, oxidative status, anti-wear additives, and nitration, amongst other measures. The current FTIR CM methodology is based on ASTM Standard Practice E2412-10 and is restricted primarily to petroleum or mineral oils; however, the ability to spectrally classify oils could overcome this limitation and broaden the scope of the methodology substantially. Quantitative FTIR methods have come to the fore to carry out more specific measures related to oil condition, specifically the determination of acid content and base content, as well as moisture. These methods have been designed to overcome some key limitations associated with the corresponding ASTM titrimetric procedures and expand the overall utility of FTIR instrumentation in centralized lubricant analysis laboratories. This paper provides an overview of FTIR CM, how it has been and can be further improved, and discusses the recent advances in the newer quantitative FTIR CM methods as well as the issues that need to be addressed to further enhance the utility of FTIR spectroscopy in relation to lubricant analysis.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".