Dynamie neural networks for jet engine degradation prediction and prognosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, fault prognosis of aircraft jet engines are considered using computationally intelligent-based methodologies to ensure flight safety and performance. Two different dynamic neural networks namely, the nonlinear autoregressive neural networks with exogenous input (NARX) and the Elman neural networks are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the jet engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine is then predicted subject to occurrence of these deteriorations. In both proposed approaches, two scenarios are considered. For each scenario, several neural networks are trained and their performance in predicting multi-flights ahead turbine output temperature are evaluated. Finally, the most suitable neural network for prediction is selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural network-based prediction and prognosis strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it