State Estimation and Fault Detection of an Electrohydrostatic Actuator
Bibliographic record
Abstract
The electrohydrostatic actuator (EHA) is an efficient type of linear actuator commonly found in aerospace applications. It consists of an external gear pump (fluid), an electric motor, a closed hydraulic circuit, a number of control valves and ports, and a linear actuator. An EHA, built for experimentation, is studied in this paper. Two types of estimation strategies, the popular Kalman filter (KF) and the smooth variable structure filter (SVSF), are applied to the EHA for kinematic state and parameter estimation. The KF strategy yields the statistical optimal solution to linear estimation problems. However, the KF becomes unstable when strict assumptions are violated. The SVSF is an estimation strategy based on sliding mode concepts, which brings an inherent amount of stability to 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. In addition to the application of the KF and SVSF for state estimation, the SVSF-VBL is applied to the EHA for the purposes of fault detection. The EHA is operated under various operating conditions (normal, friction fault, leakage fault, and so on), and the experimental results are presented and discussed.
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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".