PARAMETER IDENTIFICATION IN A HIGH PERFORMANCE HYDROSTATIC ACTUATION SYSTEM USING THE UNSCENTED KALMAN FILTER
Bibliographic record
Abstract
This paper describes an early fault detection strategy for a high performance hydrostatic actuation system, referred to as the ElectroHydraulic actuator (EHA). Safety is crucial for the EHA which is being applied in flight surface actuation systems and in robotics. The proposed fault detection methodology in this manuscript uses a new state/parameter estimation algorithm, referred to as the Unscented Kalman Filter (UKF) to estimate parameters which cannot be measured using sensors. The parameters reflect the health condition of the system and changes in their normal values can be related to the inception and progression of faults in the system. The two parameters of interest in this study are the viscous damping coefficient of a symmetrical actuator and the effective bulk modulus of the hydrostatic system. The feasibility of the approach is demonstrated by a simulation study and using experimental data. Changes in the viscous damping coefficient provide valuable information about the lubricating properties of the oil and the seal conditions of the actuator. Changes in the effective bulk modulus, as a result of air getting trapped in the system, will change the system response, affecting the natural frequency and may cause stability problems. In this paper, the UKF is used for the first time for parameter estimation in a hydraulic system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".