Hydraulic Actuator Leakage Fault Detection using Extended Kalman Filter
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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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".