Pipeline Leak Detection by Using Time-Domain Statistical Features
Bibliographic record
Abstract
Leak detection is critical for the integrity management of oil and gas pipelines. The pipeline leak can cause a major accident, especially when transporting dangerous substances. The impact to the environment and human life is paramount and thus it is essential to detect the pipeline leak in time. Usually, a leak signal from the acoustic online monitoring sensor is characterized and identified by its waveforms, absolute amplitudes, and the frequency-domain energy distribution. However, these features are not steadily available due to the propagation attenuation under varied pipeline transportation conditions. In addition, sample leak signals are needed for most existing feature extraction and modeling methods, but the actual leak signals are seldom available. Although artificially simulated leaks can be adopted alternatively, it is not possible to fully duplicate the actual leak signals with complete features. To solve these problems, this paper proposes a pipeline leak detection approach by using time-domain statistical features from acoustic sensors. These features are extracted and vectorized from normal (no leak) sample signals, which are selected by an automated method. The size of the extracted feature vector is further reduced with principal component analysis method. A support vector data description model is built with the processed vectors as the input. The proposed method has been implemented in a field leak detection system. The experimental results from the field tests demonstrate the effectiveness of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".