Enhancing Fault Detection and Isolation in All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal
Bibliographic record
Abstract
This study proposes a novel paradigm for enhancing the fault detection and isolation (FDI) of gas generators in all-electric auxiliary power unit (APU) by utilizing shaft power information from the starter/generator. First, we conduct an investigation into the challenges and opportunities for FDI that are brought about by APU electrification. Our analysis reveals that the electrification of APUs opens new possibilities for utilizing shaft power estimates from starters/generators to improve gas generator FDI. We then provide comprehensive theoretical and analytical evidence demonstrating why, how, and to what extent the shaft power information from the starter/generator can fundamentally enhance the estimation accuracy of system states and health parameters of the gas generator, while also identifying key factors influencing these improvements in FDI performance. The effectiveness of the proposed paradigm and its theoretical foundations are validated through extensive Monte Carlo simulation runs. The research findings provide a unique perspective in addressing three fundamental questions—why joint fault diagnosis of the starter/generator and gas generator in all-electric APUs is essential, how it can be implemented, and what factors determine its effectiveness—thereby opening up promising new avenues for FDI technologies in all-electric APU systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".