HEALTH ASSESSMENT FOR HYDRAULIC SERVO SYSTEM USING MANIFOLD LEARNING BASED ON EMD
Bibliographic record
Abstract
This study proposes a health assessment method for the hydraulic servo system using manifold learning based on empirical mode decomposition (EMD). An RBF neural network is adopted as a fault observer for the hydraulic servo system to generate a residual error signal. Then, the residual error signal is decomposed by EMD to form the initial feature matrix. To extract more sensitive features and reduce time consumption, isometric mapping algorithm is introduced to reduce the dimensionality of the initial feature matrix. Furthermore, the singular values of the reduced feature matrix are extracted for the subsequent health assessment. Considering the traditional Euclidean distance metric can only reflect local consistency, this study utilizes manifold distance (ManiD) to measure the health condition of the hydraulic servo system. Finally, the ManiD is converted into a confidence value, which directly represents the health status. Experiment results demonstrate the effectiveness of the proposed method.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".