Actuator and sensor fault detection and isolation for nonlinear systems subject to uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary This work addresses the problem of simultaneous actuator and sensor fault detection and isolation (FDI) for control affine nonlinear uncertain systems in the absence of measurement noise. The FDI is achieved by using a bank of filters, which utilize a subset of the measurements along with prescribed values of the control actuators to estimate states and compute expected process behavior. Residuals are next defined as the difference between the observed and expected behavior. Detectability conditions are developed, which, upon satisfaction, ensure that each residual remains sensitive to a subset of fault scenarios in the presence of uncertainty. To this end, first the ability of observers in providing bounded estimation error for a generalized class of nonlinear uncertain systems is rigorously established. These bounds allow determining thresholds that account for the impact of uncertainty on each residual. Finally, the ability of the proposed framework to achieve FDI by ensuring a unique residual breaching pattern for each fault scenario is established. The efficacy of the FDI framework subject to uncertainty and measurement noise is illustrated using a chemical reactor example.
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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