Diagnosis of partial blockage in water pipeline using support vector machine with fault-characteristic peaks in frequency domain
Bibliographic record
Abstract
Partial blockages in water pipe network can cause waste of energy and poor hygiene. Therefore, periodic diagnosis of water pipe state is necessary to maintain or replace blocked pipe when the blockage size is larger than a threshold. This work proposes a nondestructive diagnosis scheme that estimates the partial blockage in water pipe by classifying pressure signals in the frequency domain. Pressure data were collected with normal and two different fault states. A peak search algorithm is proposed to identify the ‘fault-characteristic’ peaks (FC-peaks) relevant for each blockage size. Support vector machine (SVM) classifier for each blockage was constructed with the FC-peaks as input. The SVM scores of different blockage sizes are used for diagnosis of partial blockage. The partial blockage can be diagnosed by comparing the SVM scores of different blockage sizes. The SVM classifier was able to successfully classify and diagnose three model pipes with normal state, moderate, and severe blockages.
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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".