Pipeline Leak Detection by Using Time-Domain Statistical Features
Why this work is in the frame
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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.
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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 it