A Distributed Fault Detection And Isolation Method For Multifunctional Spoiler System
Bibliographic record
Abstract
The increasing complexity of aircraft subsystems and control structure invoke new fault diagnosis methodologies for these vehicles. Multifunctional spoiler (MFS) is an essential part of an aircraft spoiler control system that can be easily deteriorated due to faults which could consequently compromise the safety of the aircraft. The MFS consists of several components with highly nonlinear dynamics. This paper presents a new fault detection and isolation (FDI) system using dynamic neural networks (DNN) to deal with incipient faults at their early stages. For this purpose, an intelligent distributed FDI framework consisting of three DNNs is employed for generating residual set in the system to observe any discrepancy in the states of the system. Furthermore, the dynamic structure of the designed neural networks helps the observers tackle the non-linearity of the system and provides the fault isolation in the whole operating range. Simulation results are conducted to demonstrate the ability and effectiveness of the proposed FDI system.
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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".