Failure Mode and Effect Analysis and Why It Should Be Considered When Establishing a Condition Monitoring Program and Writing ASTM Test Methods
Bibliographic record
Abstract
Following the decision to implement a lubricant condition monitoring program, a test protocol needs to be put in place. Some tests such as wear metal determination or oil viscosity are needed for most machines. Other possible tests may not be as obvious. Practitioners may consider soliciting recommendations from a test laboratory or consulting an industry benchmark standard such as ASTM D6224, Standard Practice for In-Service Monitoring of Lubricating Oil for Auxiliary Power Plant Equipment, which provides recommendations in a tabular format. Although both of these options may yield a quality condition monitoring program, it is likely that neither would result in an optimal lubricant testing program. The best program would be one that used tests that pertained to how the machine fails, have early failure detection capability, and allow for monitoring failure progression. Performing a failure mode and effects analysis (FMEA) results in an understanding of how a machine fails and how well lubricant analysis can be expected to identify a particular failure mode. This understanding can also be leveraged into the selection of a particular method over other similar methodologies. This may also be useful when determining how often to perform the test. ASTM has developed a series of standards that detail the application of the FMEA lubricant analysis condition monitoring and testing process.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.021 |
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".