Internal Leakage Detection in Hydraulic Actuators Using Empirical Mode Decomposition and Hilbert Spectrum
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Bibliographic record
Abstract
The applicability of Hilbert-Huang transform (HHT) for internal leakage detection in valve-controlled hydraulic actuators is investigated in this paper. First, the actuator response to structured (periodic step) inputs directly applied to the control valve is analyzed. This procedure is a representative of an offline diagnosis scheme. Next, the capability of the approach toward online applications, whereby the actuator tracks unstructured (pseudorandom) position reference inputs in a closed-loop control scheme against a load, is examined. The pressure signal at one side of the actuator is decomposed into oscillatory functions called intrinsic mode functions (IMFs), and Hilbert transform is applied to each IMF to obtain the instantaneous amplitude. It is shown that the root mean square of the instantaneous amplitude associated with the first IMF establishes feature patterns that can be effectively used to detect internal leakage and its severity. Experimental tests show the effectiveness of the approach in detecting internal leakage values as low as 0.124 L/min (representing a reduction of approximately 2.6% of the available flow rate to move the actuator) during offline diagnosis and as low as 0.23 L/min (representing a reduction of approximately 5% of the available flow rate to move the actuator) when the actuator tracks reference position inputs online. This is done without having prior knowledge about the model of the actuator or 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