Linking MRO to Prognosis Based Health Management Through Physics-of-Failures Understanding
Bibliographic record
Abstract
Traditional engine maintenance, repair and overhaul (MRO) are geared toward fixed schedules. However, with online condition monitoring, assessments and prognosis, it is required that MRO be adaptive to the life consumption with respect to the actual usage of the engine to realize the benefit of prognosis. Shifting to this new paradigm, there are several challenges: 1. How exactly the life is consumed in components under complex usage profiles that may involve a combination of low and high cycle fatigue, thermomechanical fatigue and creep, for regular usage (aside from incidents)? 2. What are the physical failure mechanism(s) in components under the above conditions, the understanding of which may help to select the most appropriate detection, repair and replacement (including material insertion) strategy, for life renewal/extension and cost reduction? 3. Understanding the limitations of repairs for the particular failure mechanism. To overcome these challenges, one needs physics-based material failure models that can reflect the true failure mechanisms on the component level under actual (complex) usage profiles. Conventional, empirical test-data correlations for isolated conditions fall short of this requirement because pure fatigue and pure creep are not experienced by real components in service. In addition, to ensure the repaired component’s structural integrity, the material database would have to be comprehensive enough to cover the effect of intrinsic repair defects and microstructures that are not present in the original component material. While models and data are integral parts of digital twins, it is proposed that physics-based models are needed to cover the entire application domain continuously. This paper will introduce a physics-based method of life consumption evaluation and discussion through past experience in line with the development of Digital Twin concepts for sustainment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".