Fault Detection of an Electrohydrostatic Actuator With the SVSF Time-Varying Boundary Layer Concept
Bibliographic record
Abstract
The electrohydrostatic actuator (EHA) is an efficient type of actuator commonly used in aerospace applications. It makes use of a closed hydraulic circuit, a number of control valves, an electric motor, and a fluid pump (usually a type of gear pump). The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode concepts formulated in a predictor-corrector fashion. The SVSF offers a number of advantages over other traditional estimation methods, including robust and stable estimates, and an additional performance metric. A fixed smoothing boundary layer was implemented in an effort to ensure stable estimates, and is defined based on the amount of uncertainties and noise present in the estimation process. Recent advances in SVSF theory include a time-varying smoothing boundary layer. This method, known as the SVSF-VBL, offers an optimal formulation of the SVSF as well as a method for detecting changes or faults in a system. This paper implements the SVSF-VBL in an effort to detect faults in an EHA. The results are compared with traditional Kalman filter-based methods.
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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".