Enhancing Pipeline Monitoring: Optimizing Window Size with Monte Carlo Search and CB-AttentionNet
Bibliographic record
Abstract
Pipeline monitoring is crucial for preventing severe environmental and economic losses. Therefore, accurate and timely leak detection is essential. Deep learning has become a vital tool for analyzing time-series data to detect pipeline leaks. A key parameter in this analysis is the window size, which refers to the duration of data segments used for processing within the model. Fixed window sizes often fall short when dealing with dynamic and variable-length sequential data. This research advances a probabilistic search framework called Monte Carlo methods to adapt to the dynamic characteristics of pipeline signals. We systematically optimized window sizes ranging from 3 to 90 seconds using a large volume of industrial pipeline data. Our findings indicate that moderate window sizes, particularly between 45 and 60 seconds, provide an effective balance between reducing misclassified leaks and maintaining high training accuracy. Furthermore, our analysis of resource usage and evaluation times demonstrates that the model's performance is efficient and manageable within the constraints of typical operational environments.
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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".