An actuator fault isolation strategy for linear and nonlinear systems
Bibliographic record
Abstract
This paper investigates actuator fault isolation problem for linear systems. Then, as an extension, actuator fault isolation problem for nonlinear systems is considered next For both linear and nonlinear systems, two cases are studied. In the first case, we assume that all states are available. The second case assumes that only outputs are available. To accomplish fault isolation, we use a bank of observers for all possible faulty models. The paper considers constant actuator faults, that is, the outputs of some actuators are stuck at fixed undesirable constant values. The fault isolation strategy is based on combining the conventional observer design techniques with adaptation techniques. Based on the designed observers, a bank of residuals are defined correspondingly. The actuator faults can be isolated if only one residual goes to zero while the others do not. The faulty model with residual approaching zero identifies the faulty actuators. For linear and nonlinear systems with all states available, new sufficient conditions for fault isolation are derived, which require only that the distribution matrices of the actuators are of full column rank. For linear and nonlinear systems with only outputs available, new sufficient conditions for fault isolation are also derived, which require additional conditions besides the full column rank condition of the distribution matrices of the actuators. Some simulation results are given to show the effectiveness of the proposed fault isolation methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".