Physics-based Model and Neural Network Model for Monitoring Starter Degradation of APU
Bibliographic record
Abstract
An electric starter provides the initial power to run the Auxiliary Power Unit (APU) during the startup process. With the starter degradation, its output power declines, which affects the APU starting performance, and eventually leads to the starting failure. Previous works have attempted to estimate the starter degrading trend, however a clear symptom for the starter degradation is not provided, which can be instrumental for the preventive maintenance. This paper develops a physics-based transient model to assess the starter degradation using the gas-path measurements of the APU. To overcome shortcomings for the lack of component characteristics, a generic modeling approach is adopted. For the comparative study, a back-propagation, feedforward neural network model is structured, trained and tested. Both models are implemented in the nominal and degraded conditions, and their capabilities as monitoring tools for the starter degradation are verified. The physics-based approach provides more accurate results for the cases with degraded starters, whereas the neural network model shows superior results with the starters in healthy condition.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".