Using Dynamic Pressure Response for Erosion Detection in Hydraulic Tubes and Hoses
Bibliographic record
Abstract
Abstract Numerical simulations were performed to study the feasibility of erosion detection in hydraulic tubes and hoses using fluid dynamic pressure response analysis. Reflected pressure signals caused by wall thinning were studied to locate and quantify pipe defects. Simulations were conducted for steel pipes as well as hoses. Results showed that for a steel pipe, since the stiffness of the fluid is much less than the pipe material’s, a very big change of wall thickness is needed to have a meaningful change in wave propagation speed and therefore the dynamic pressure response. For hoses, the wall stiffness is much less than steel pipes, hence it is more feasible to detect changes in stiffness. A dataset of 10 000 dynamic pressure impulse responses from samples with randomly generated eroded geometries was calculated to train a gated recurrent unit (GRU) neural network. Results showed that under perfect conditions (no noise), we are able to detect an eroded section’s location, length, and change in wave propagation speed with relative errors of 2.69%, 4.88%, and 3.79%, respectively. The changes in the wave propagation speed was also categorized into three classes of low, mild, and severe erosion with the accuracy of 97.3%. Under more practical conditions including sensor noise, the accuracy of erosion detection is degraded, especially in the case of steel tubing. By retraining the model with noisy data, the drop in the accuracy is compensated to about 96%.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".