Spectral Domain Dynamic Pressure Model and Wear Simulation of Axial Piston Pumps
Bibliographic record
Abstract
Axial piston pumps work as power supply units in hydraulic systems, assigning great significance to their health management. Model-based method arouses more attention for the inherent physical interpretability. However, most pressure models are confronted with the nonlinear and ill-conditioned terms, posing challenges in obtaining solutions. To address this issue, a spectral domain model is established for simulating dynamic pressure at the outlet port of axial piston pumps. Firstly, pressure build-up equations are derived at the outlet port. Then, Fourier expansions are conducted on the terms in the equations, converting the differential equations into algebraic ones with respect to Fourier coefficients. Additionally, the wear factor is introduced in the proposed model to simulate the faulty response. The experiments involve testing varying degrees of piston wear, demonstrating the feasibility of simulating wear-induced faults. The proposed model successfully captured the increase in rotational frequency amplitude under progressive wear conditions, providing a physical explanation for the fault characteristic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".