Investigation of Multiphase Flow Leak Detection in Pipeline Using Time Series Analysis Technique
Bibliographic record
Abstract
Abstract Detecting chronic small leak sizes can be challenging because they may not produce significant or easily noticeable changes in flow rates or pressure differentials. Therefore, specialized techniques are often required to identify and locate chronic small leaks accurately in pipeline systems. The current study aims to address this gap by developing a method to identify multiphase flow leaks in pipelines using time series analysis techniques. An experimental flow loop apparatus, featuring a 2-inch (0.0508 m) diameter and extending 22.6 feet (6.9 m) in length, has been employed to carry out our experiments. The experiments encompass a range of liquid flow rates varying between 170 and 350 kg/min and gas flow rates ranging from 10 to 60 g/min. The system was equipped with three distinct leak opening diameters, measuring 1.8 mm, 2.5 mm, and 3 mm, each separated by 90 mm. Data collected from four dynamic pressure sensors was subjected to time series analysis such as wavelet transforms to detect and pinpoint the location of pipeline leaks. The obtained results indicate that dynamic pressure sensors are effective in detecting leak scenarios, as well as distinguishing between single and multiple leaks. However, for chronic small leaks, analyzing the standalone pressure response over time is generally not sufficient for detection. Time series analysis techniques play a crucial role in accurately identifying chronic small sized pipeline leaks. Discrete Wavelet Transform (DWT) was able to identify the point of leak opening and closing. Furthermore, DWT was able to reduce the false alarms for leak and no leak situations. This study introduces the application of time series analysis on dynamic pressure to detect chronic small sized leaks in multiphase flow pipelines. Additionally, it explores the capacity of wavelet analysis to minimize the occurrence of false alarms for leak and non-leak scenarios thereby addressing crucial safety, environmental, and economic concerns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".