Internal Leakage Detection in Hydraulic Actuators Using Empirical Mode Decomposition and Hilbert Spectrum
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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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 | 0.001 |
| 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 | 0.001 |
| 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 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".