A Novel PPA Method for Fluid Pipeline Leak Detection Based on OPELM and Bidirectional LSTM
Bibliographic record
Abstract
Pipeline leak detection has attracted great research interests for years in the energy industry. Continuous pressure monitoring is one of the most straightforward approaches in leak detection which utilizes pressure point analysis (PPA) algorithms to exploit the transient pressure characteristics and identify leak events. However, a critical issue that jeopardizes the deployment of PPA based methods is the high false alarm rate. In this paper, a novel PPA based leak detection method is proposed which can accurately detect the leak events and dramatically decrease the number of false alarms compared to existing methods. Firstly, the proposed method takes advantage of the good approximation ability and fast learning speed of optimally-pruned extreme learning machine (OPELM) to produce a preliminary leak detection result. Then, the strong memorizing ability of bidirectional long-short term memory (BiLSTM) network is exploited to identify the true positive from the preliminary detection result, hence significantly decrease the number of false alarms. Furthermore, a feature extraction mechanism is also proposed to obtain both the dynamic and static characteristics from raw pressure wave. Experiments and verifications are performed on different real world data sets obtained from pipeline leak tests. It shows that the proposed method can achieve higher detection accuracy with significantly less false alarms. It enhances the practicality of pressure monitoring based leak detection schemes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".