Robust fault detection and isolation via a diagnostic observer
Why this work is in the frame
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Bibliographic record
Abstract
We present two methodologies for the design of robust fault isolation observer for linear uncertain systems. The proposed fault isolation observer is robust to structural uncertainties by producing disturbance decoupled residuals. The first method uses a direct eigenstructure assignment scheme to accomplish a diagonal transfer function between the faults and the residuals. The second method is carried out through transformation of the linear system under consideration into its special coordinate basis (SCB) form. Once the system is in SCB form, we propose a disturbance decoupled fault detection observer (DDFDO) which is combined either with Beard–Jones detection filter (BJDF) theory, or input estimation results. This will lead to the final proposed robust fault detection filter. Finally, two numerical examples are given in order to illustrate the validity and effectiveness of the proposed fault detection and isolation (FDI) strategy. Copyright © 2000 John Wiley & Sons, Ltd.
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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 it