Leakage Fault Detection in Hydraulic Actuators Subject to Unknown External Loading
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Bibliographic record
Abstract
This paper describes development and experimental evaluation of a hydraulic actuator leakage fault detector based on the extended Kalman filtering (EKF). Identification of external leakage at either side of the actuator as well as the internal leakage between the two chambers is examined. The present work is built upon previous work by the authors, but incorporates a significant improvement in that the new scheme is capable of detecting leakage faults for actuators that are also subject to unknown loading and/or significant friction. Experiments on a laboratory-based hydraulic actuator, using both structured (sinusoidal) and unstructured (pseudorandom) test signals show that: (i) under normal (no- fault) operating condition, the EKF-based state estimator closely predicts the states of the system and the external load, including actuator friction, using only a few measurements, (ii) in the presence of leakage faults, the level of residual errors between the estimated and the measured line pressures increase indicating the occurrence of faults and (iii), different leakage fault types and levels can be identified by tracking the pattern of the residual errors and without a need to model leakage faults. The present work lays a foundation for developing on-line leakage monitoring systems for hydraulic actuators.
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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 | 0.001 | 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 it