State estimation of a faulty actuator using the second-order smooth variable structure filter (The 2<sup>ND</sup>-order SVSF)
Bibliographic record
Abstract
This paper presents the application of the new developed second-order Smooth Variable Structure Filter, 2nd-order SVSF, for fault detection under uncertain conditions. The 2nd-order SVSF is a novel modelbased state estimation method formulated in a predictor-corrector form. It produces robust state estimation under uncertain conditions, while decreasing the measurement error and its difference, at the same time. The stability of the 2nd-order SVSF is proven using the Lyapunov's stability criterion with the given bounded noise and modeling uncertainties. The corrective gain of the filter is pushing the measurement error and its difference toward zero. This results in providing higher degrees of accuracy, robustness and smoothness in state estimation under uncertain situations. Due to the robust performance of the 2nd-order SVSF for state estimation, it applies to an experimental setup of an Electro-Hydrostatic Actuator (EHA) for fault detection. Experimentation are performed for the EHA setup under the normal and faulty conditions. For the faulty EHA setup, there exists a major friction condition in the EHA's cylinders. The robustness of the new developed 2nd-order SVSF is then verified by comparing its performance with the state-of-the-art filters including the Kalman Filter (KF) and the 1st-order SVSF.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".