Adaptive Sensor Fault Detection and Isolation in Uncertain Systems
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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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".