Bibliographic record
Abstract
Auxiliary power unit (APU) is a gas turbine engine on aircraft that provides energy for functions other than propulsion.Its starter is a crucial component that outputs assistant power to support the APU starting process.Starter performance degradation significantly impairs the whole APU life and raises risks for the aircraft flight.However, the current maintenance policy for the starter is still "run it till it breaks".An effective technique for the starter diagnostics and prognostics has not been reported yet.The aim of this thesis is to propose a framework for enabling the online detection and prediction of starter degradation.For this purpose, the thesis makes use of a dataset containing information about 52 APU "inability to start" failure events that were collected from actual aircraft operations over a period of ten years.Through the establishment of the relationship between the starter degradation and gas turbine engine starting performance, 13 of these 52 failures were identified as being caused by the starter degradation.Once this determination has been made, an online classifier based on moving autocorrelation is designed to detect the initial phase of degradation for each failure.Finally, a particle filtering based approach with an associated system state model is proposed to achieve the fault diagnostics and failure prognostics.The results demonstrate that a condition based maintenance program for the APU starter can be implemented to avoid unnecessary economic losses and to enhance aircraft operating safety.i
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".