Application of sliding mode observers for actuator fault detection and isolation in linear systems
Bibliographic record
Abstract
This paper investigates the actuator fault isolation problem for a class of linear systems. To isolate the actuator faults amongst all possibilities, we first relate each possibility to a faulty model, then for each possible faulty model, a sliding mode observer (SMO) is designed. It is proved that, for the faulty model corresponds to the faulty actuators, the SMO designed can ensure the related state estimation error and thus the output estimation error goes to zero. It is also shown that, for all other possible faulty models, none of SMOs can make the related output estimation errors be zero. Based on the results proved, we define the residuals as the square of the magnitudes of the output estimation errors resulting from all possible faulty models. If only residual goes to zero, then it corresponds to the faulty actuators, and actuator fault isolation is done. The use of SMOs has two advantages. One is that it can deal with any types of bounded actuator faults (constant and non-constant faults); the other is that it can provide a method to estimate the faults. The actuator fault isolation method is tested on a research civil aircraft model (RCAM), and simulation results show that it can isolate various types of actuator faults effectively
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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".