Hydraulic Actuator Leakage Fault Detection using Extended Kalman Filter
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the application of extended Kalman filter (EKF) in order to identify leakage faults in hydraulically powered actuators. A hydraulic actuator can suffer from two types of leakages: internal or cross-port leakage at the piston seal and, external leakage at the shaft seal or the connecting pipes. An EKF-based estimator is constructed that includes complete nonlinear models of hydraulic functions as well as inevitable stick-slip friction in the actuator. It is shown that, firstly, under normal (no-fault) operating condition, the developed estimator closely predicts the states of the system, using only a few basic measurements. Secondly, in the presence of leakage faults, the level of residual errors between the estimated and the measured line pressures, increase significantly indicating the occurrence of faults. Thirdly, different leakage types can be identified by mapping the residual errors changes. Experiments are performed on a laboratory-based hydraulic actuator circuit. The results demonstrate the efficacy of the proposed EKF-based fault detection scheme to promptly and reliably respond to actuator's external and internal leakage faults.
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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) | 0.001 | 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 it