A Wavelet-Based Approach for External Leakage Detection and Isolation From Internal Leakage in Valve-Controlled Hydraulic Actuators
Bibliographic record
Abstract
In this paper, the application of wavelet transform to detect external leakage fault in hydraulic actuators is described. This paper also examines the isolation of this fault from actuator internal leakage in a multiple-fault environment. The pressure signal at either chamber of a laboratory-based hydraulic actuator, in response to a periodic step input, is decomposed into discrete wavelet coefficients. An index is calculated based on the root mean square (rms) value of level-four approximate coefficient of the pressure signal. This index is shown to be sensitive to external leakage. Furthermore, in our previous work, an index was calculated based on the rms value of the level-two detail coefficient for internal leakage fault detection. In this paper, we further show that these indices are independently sensitive to external and internal leakages. Thus, by inspecting them simultaneously, one cannot only identify external and internal leakages but also isolate them even when they occur together. Experimental tests show promising results for detecting and isolating low amount of external and internal leakages without a need to model the actuator or leakage types.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".