Simulation of the behaviour of engines in their current state of wear
Bibliographic record
Abstract
Aeronautical data is increasingly available thanks to new ways of downloading and storing. In addition, the computer clusters on which we capitalize this data become effective for the implementation of machine-learning and artificial intelligence calculations. However, our companies, which focus on aeronautical technical solutions, find it difficult to exploit this data and to take advantage of the academic skills of our university laboratories. Indeed, these same data remain the property of the airlines and are under contract, unfortunately preventing them from being exchanged. Indeed, whenever we present the results of algorithms developed in-house, the laboratories insist that we open the data that would enable them to demonstrate the effectiveness of their methods. At Safran Aircraft Engines, we were able to demonstrate the effectiveness of using time series downloaded from the engines after each flight to build a representative model of the engine using a conditional generative neural algorithm (CGAN). Subject to the flight conditions and controls, this model simulates the behaviour of the engine in its current state as if it had performed the simulated flight. It is therefore possible for us to provide virtual flights performed by our engines in their actual state of wear. These simulations pave the way for sharing open datasets, which will, we hope, influence research for the discovery of new techniques for monitoring our engines.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".