Internal Leakage Diagnosis in Hydraulic Actuators Using Wavelet Transforms
Bibliographic record
Abstract
This paper describes experimental evaluation of applying wavelet transform to detect internal leakage in hydraulic actuators due to seal damage. The method analyses pressure signal at one side of the actuator in response to periodic step inputs to the control valve. It is shown that the detailed version of decomposed pressure signal, using discrete wavelet transform, establishes feature patterns that can effectively detect internal leakage and its severity. The method is also capable of detecting changes in the friction property of the actuator, another problem associated with actuator seal damage. Specifically, the root mean square (RMS) of level two detail coefficients, obtained from the measured pressure signal, is used to detect internal leakage. The degree of changes of the RMS value from the one obtained under normal operating condition indicates the severity of leakage fault. Furthermore, it is shown that the RMS of level three detail coefficient values, is sensitive to the changes of the actuator friction. All these observations are made without a need to model the actuator, leakage or friction.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".