Leakage Fault Detection in Hydraulic Actuators Subject to Unknown External Loading
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".