Viscous Damping Coefficient and Effective Bulk Modulus Estimation in a High Performance Hydrostatic Actuation System using Extended Kalman Filter
Bibliographic record
Abstract
Increasing demands on reliability and safety of fluid power devices have brought much attention to methods for improving condition monitoring of these devices. Whereas faults in hydraulic systems were detected only when limit values of measurable output signals were transgressed, recently, attempts have been made to detect them earlier and to locate them better by the use of measurable signals. The Extended Kalman Filter can be used for real-time estimation of parameters in system models. Changes in model parameters may be tracked and, in turn, be used for determining the condition of the system. In this paper, the Extended Kalman Filter (EKF) is applied to a novel hydrostatic actuation system referred to as the Electrohydraulic Actuator (EHA). A state space model of the EHA is developed and the Extended Kalman Filter is used to estimate unmeasurable but critical parameters such as viscous damping coefficient of the actuator and the effective bulk modulus of the system. The proof of concept of applying the EKF for parameter and state is demonstrated through both simulation and experimental evidence. Changes in the viscous damping coefficient at the actuator at a known temperature may be good indication that the fluid is degrading or that the dynamic seal of the actuator is experiencing wear. The effective bulk modulus has a large impact on the system response, affecting the natural frequency and stability and can have implications on the safety of operation. These two parameters cannot be measured directly and hence need to be estimated. Based on this estimation, corrective actions may be taken in safety critical applications for the EHA such as Flight Surface Actuation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".