Pipeline Leak and Impact Detection System - PipeLIDS - Monitoring Product Dedicated to Onshore Pipelines
Bibliographic record
Abstract
Abstract Onshore pipelines may be subjected to third party damage (civil works), corrosion leaks, mechanical issues (valve or pumps failures), land movements or (in extreme cases) theft of the product. These threats may lead to leaks or even more dramatic explosions. The impact on human life and the environment can be significant. The pipeline operator's public image can be damaged with non- negligible economic or political consequences. PipeLIDS has been developed to minimize the consequences of leaks from onshore pipelines. It uses acoustic technology for detection of noise generated by impacts or leaks. This noise propagates in both directions over long distances inside the pipe. Intrusive acoustic sensors are installed regularly on the pipeline (every ten kilometers) to detect this noise. Each sensor is connected to an intelligent beacon with GPS synchronization. By measuring the sound wave's amplitude and phase time shift and analyzing the acoustic signature, PipeLIDS detects the origin and location of the event and raises an alarm. This paper presents latest technical improvements implemented on the PipeLIDS as installed on the latest SPSE Fos-sur-Mer (France) diesel pipeline for permanent survey. Acoustic sensors have been redesigned for better reliability. Software was reviewed and updated to give more efficient data processing, detecting and locating any accidental events within few minutes while being able to eliminate false alarms. Finally, very good performances of the system is demonstrated as well as its simplicity in terms of installation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".