Sliding mode output estimator based fault detection, isolation and estimation for systems with unmatched unknown inputs
Bibliographic record
Abstract
This paper considers fault detection, isolation and estimation problems for a class of systems with unknown inputs which may not satisfy certain matching conditions. Such conditions are often required for existence of sliding mode observers (SMO). In cases when the unmatched unknown inputs are present, no SMO could be designed such that the state estimation error is invariant to all the unknown inputs, and therefore, existing SMO based fault diagnosis schemes can not be employed. In this article we propose a novel approach to design of output estimators using sliding mode technique. The estimators are then used for fault detection, isolation, and estimation. First, a canonical representation of the system which decouples the matched and unmatched unknown inputs is derived. Second, based on this canonical form, output estimators using sliding mode technique are proposed, and their properties are investigated. Third, a fault diagnosis scheme is developed to carry out the fault detection, isolation, and estimation tasks. Finally, an example is given to show the effectiveness of the output estimator based fault diagnosis scheme in terms of fault detection, isolation and estimation.
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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".