A Comprehensive Literature Review on the Resolution of Turbine Engine Performances' Inverse Problems
Bibliographic record
Abstract
Abstract Turbine engine monitoring is a well-known and well-studied subject that proves to be essential for the aeronautic industry. A popular approach in engine monitoring is constructing indicators that reflect systems' health states by leveraging operational measurements (i.e., sensors' data during flights)—this is known as the engine performance's inverse problem. There exists an extensive literature on this topic, especially revolving around two well-used types of performance indicators of aircraft engines: efficiencies and air mass flow rates of engine's modules. This review aims to provide a comprehensive survey of this particular literature, which so far has not been properly organized and structured. Our first contribution is to propose a novel taxonomy of the relevant methods. In particular, we consider the role of physics-based models—an element that provides specific advantages and challenges in the context of aircraft engines monitoring—and see if each method exploits such models inside or outside the main algorithmic process (or not exploiting them at all). Our second contribution is to identify the pros and cons of each method, along with additional insights with respect to two commonly encountered challenges: under-determined scenarios and time-series data. Finally, we give some guidelines for selecting appropriate strategies in practical situations and perspectives for future works.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".