Condition Monitoring of an Electro-Hydrostatic Actuator Using the Dynamic 2nd-Order Smooth Variable Structure Filter
Bibliographic record
Abstract
This paper introduces the dynamic 2nd-order Smooth Variable Structure Filter (Dynamic 2nd-order SVSF) method for the purpose of robust state estimation. Thereafter, it presents an application of this method for condition monitoring of an electro-hydrostatic actuator system. The SVSF-type filtering is in general designed based on the sliding mode theory; whereas the sliding mode variable is equal to the innovation (measurement error). In order to formulate the dynamic 2nd-order SVSF, a dynamic sliding mode manifold is defined such that it preserves the first and second order sliding conditions. This causes that the measurement error and its first difference are pushed toward zero until reaching the existence subspace. Hence, this filter benefits from the robustness and chattering suppression properties of the second order sliding mode systems. These help the filter to suppress the undesirable chattering effects without the need for approximation or interpolation that however reduces accuracy and robustness of the SVSF-type filtering. In order to investigate the performance of the dynamic 2nd-order SVSF for state estimation, it applies to an Electro-Hydrostatic Actuator (EHA) system under the normal and uncertain scenarios. Simulation results are then compared with ones obtained by other estimation methods such as the Kalman filter and the 1st-order SVSF method.
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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) | 0.000 | 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) | 0.001 | 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".