Multi‐Sensor Fusion for Transient‐Based Pipeline Leak Localization in the Dempster‐Shafer Evidence Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Detecting a small leak in a water‐supply pipe with a wave scattering power of the order of noise or smaller is a challenging problem because its signature in a measured signal is weak. Experimental data show that multiple sensors enhance the evidence about a leak in a transient test and, thus, increase the possibility of successful leak detection in noisy environments. Therefore, a leakage localization scheme is proposed, which fuses multi‐sensor measurements in the Dempster‐Shafer evidence framework. The signature of a leak in each measurement is extracted and translated into a piece of evidence regarding its presence and location. Then, the pieces of evidence from different sensors are fused using the Dempster's rule of combination to form a unified leak location estimation. The proposed method is model‐free and is thus insensitive to imprecise knowledge of pipe system. The gain of the multi‐sensor fusion mechanism on the leakage localization accuracy is demonstrated via both numerical and experimental data.
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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 | 0.000 |
| 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 it