Internal Leakage Detection in Electrohydrostatic Actuators Using Multiscale Analysis of Experimental Data
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Bibliographic record
Abstract
One of the main faults that may happen in electrohydrostatic systems is the actuator internal leakage that occurs due to wearing in the piston seal. This paper focuses on detecting the internal leakage using multiscale analysis of experimental data measured from an electrohydrostatic actuator (EHA) test rig. Multiscale techniques are the strong tools in analysis of time series, as they are able to extract more useful information about dynamical systems as compared with single-scale methods. In this paper, several multiscale measures are obtained from the actuator pressure signal of an EHA testbed in both healthy and faulty operating modes. The measures are correlation fractal dimension, variance fractal dimension, maximal Lyapunov exponent, average value of correlation entropy, and wavelet detailed and approximation coefficients. Sensitivity of each measure to the effect of the internal leakage is quantified by calculating the percentage of change of faulty measures with respect to those of the healthy operating mode. The percentage of change in the mean value of correlation entropy and level five wavelet detailed coefficient indicated that these two measures are appropriate indicators to detect different levels of actuator internal leakage in EHA systems. In contrast, the correlation fractal dimension, the variance fractal dimension, and maximal Lyapunov exponent did not exhibit reliable sensitivities to the internal leakage.
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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) | 0.000 | 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