A Wavelet-Based Approach to Internal Seal Damage Diagnosis in Hydraulic Actuators
Bibliographic record
Abstract
This paper describes the application of wavelet transform (WT) to detect internal leakage in hydraulic actuators, caused by seal damage. The method analyzes the 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 WT, establishes feature patterns that can effectively detect internal leakage and its severity. The proposed scheme requires a baseline (threshold) value, predetermined first by analyzing the pressure signal of a healthy actuator. Once the root mean square (rms) of the level-two detail coefficient values, obtained from the measured pressure signals in subsequent offline tests, fall below this baseline, a fault alarm is triggered. Furthermore, the degree of changes of the rms value from the one obtained under normal operating condition indicates the severity of fault. Experimental tests show promising results for detecting internal leakages as low as 0.124 L/min, representing approximately 2.6% reduction of flow rate available to move the actuator. This is done without a need to model the actuator or leakage. Other methods of leakage fault diagnosis require the model of the actuator or leakage fault. Furthermore, no other method reported the internal leakage detection of magnitude as low as the one reported in this paper.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".