Detecting Pipeline Leaks Using a Novel Data-Driven Statistical Methodology with Earth Mover's Distance
Bibliographic record
Abstract
Abstract This study introduces a novel leak detection methodology for liquid pipelines, combining a Sequential Probability Ratio Test (SPRT) with a Kantorovich Distance (KD) filter (SPRT-KD). This model enhances leak detection performance over traditional volume balance (VB) systems in the traditional domains of reliability, sensitivity, robustness, and accuracy. The model performs well in complex pipeline networks in real time, addressing limitations of conventional methods under transient conditions. The SPRT-KD integrates a statistical data-driven SPRT model based on volume imbalance for statistical leak detection with a KD filter to detect operational changes. SPRT accumulates evidence of leaks via flow discrepancies, employing slow, medium, and fast time window detection filters, while KD measures shifts in the flow distribution to detect and suppress the effect of transients which could cause false alarms. The process involves continuous monitoring of flow, KD-based anomaly detection, and SPRT score aggregation over short period (15-minute) windows to trigger alarms when thresholds are exceeded. In a case study from a Northern Alberta condensate pipeline (December 2023), the SPRT-KD model detected a 40 m3/day leak (0.6% of 6700 m3/day total flow) in 13 minutes, compared to 3 hours for a VB model, despite high transience in the complicated network. The KD filter eliminated false alarm signals attributed to operational changes. The model maintained signal integrity, with no loss of alarm signal over the entire 10 hour leak period. SPRT-KD demonstrated superior sensitivity (detecting leaks below the flow meter accuracy) and speed compared to a VB model. The SPRT-KD model combines a sensitive data-driven model with the use of a real time rolling KD calculation to identify operational changes. This approach minimizes false alarms in real-world environments, even in highly transient networks with a plurality of inlets and outlets. SPRT-KD offers a significant advancement in statistical leak detection, providing rapid, reliable alerts in transient environments, validated by real-world application.
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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".