Nonlinear Geometric Approach to Fault Detection and Isolation in an Aircraft Nonlinear Longitudinal Model
Bibliographic record
Abstract
In this paper, aircraft actuator fault detection and isolation (FDI) is investigated and designed using a nonlinear geometric FDI approach based on a nonlinear longitudinal aircraft model. Two detection filters are designed for the throttle position and the elevator angle, respectively, which are the two main actuation signals in the longitudinal aircraft model. In nonlinear geometric FDI approach the objective is to find state and output transformations, if such transformations exist, that lead us to a new set of observable states which are unaffected by all faults but one. Numerous simulation results show the excellent performance of the designed nonlinear diagnosis filters in detecting and isolating certain types of faults such as float and loss of effectiveness in the input channels. Comparative simulation results are conducted to demonstrate the superiority of the proposed nonlinear filters to their linear counterparts. It is shown that the linear geometric approach may fail to detect and isolate the above faults mainly due to the model inaccuracies that are inherent to the linearization of the nonlinear aircraft model.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".