Adaptive Sensor Fault Detection and Isolation in Uncertain Systems
Why this work is in the frame
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Bibliographic record
Abstract
An adaptive sensor fault detection and isolation approach in linear multi-input multi-output (MIMO) systems with unknown system parameters is presented. The proposed diagnostic approach abandons the idea of designing adaptive observers to estimate the system's state, and rather employs the design of adaptive output estimators for estimating only the outputs. First, a MIMO system is decomposed into a group of MISO systems and a transfer function description for each MISO system is presented. Second, based on each transfer function and for each output, an output equation, which is suitable for output estimator design, is obtained by filtering the corresponding output and all the inputs properly. Third, using the derived output equations, adaptive output estimators are designed for all outputs. Finally, based on the designed output estimators, the adaptive sensor fault diagnosis problems are solved. The proposed fault diagnosis scheme enables us to treat each output separately, and this turns the difficult sensor fault isolation problem into a much simpler task. Another advantage offered by the proposed approach is that it does not require the original systems to be detectable. The results presented in this respect are even new for known linear MIMO systems because no such scheme has been proposed in the literature in the past. A linearized aircraft model is used as an example to show the effectiveness of the output estimator based fault diagnosis scheme in terms of sensor fault detection and isolation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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