A Wavelet-Based Approach for Diagnosis of Internal Leakage in Hydraulic Actuators using On-Line Measurements
Bibliographic record
Abstract
Prompt diagnosis of faults associated with hydraulic actuators is important to maintain reliability and performance and to avoid complete loss of functionality. This paper presents new development and evaluation of a wavelet-based method, intended for on-line detection of internal leakage in a valve-controlled hydraulic actuator. This work is built upon the initial study by the authors, in which actuator's internal leakage was detected using limited-duration data on one of the actuator's chamber pressures, in response to a structured input signal and under no load condition. In the present work, the more realistic case of an actuator that is driven in a closed-loop mode to track pseudorandom position references is considered. Additionally, the actuator is subject to loading. Furthermore, limited-duration pressure signals are obtained using a sliding window technique applied to the stream of on-line measurements. It is shown that the root mean square values of level two detail coefficient vectors of pressure signals collectively establish a feature index that can effectively detect internal leakage. This monitoring index is shown to decrease in magnitude and energy once the leakage occurs. Extensive validation tests are performed to demonstrate the effectiveness of the proposed technique in detecting internal leakage, given any reference step input or loading condition. The significance of the proposed method is that it does not need models of the actuator and leakage fault or any baseline information on performance of the healthy actuator. Furthermore, the method remains effective even with control systems that are tolerant to leakage fault. Finally, the method can detect low internal leakages, in the range of 0.2 to 0.25 l/min, not reported in any of the previously published work. These aspects make the method very attractive from the industrial implementation viewpoint.
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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".