Optimization of Averaging Window Length and Alarming Hold Time in Volume Balance RTTM Leak Detection to Minimize False Alarms and Spill Volume
Bibliographic record
Abstract
Reliability and sensitivity are two main performance metrics of leak detection systems as defined by API 1130 [1]. Proper thresholding scheme is one of the primary factors in having a sensitive and reliable leak detection system with timely detection. In RTTM leak detection, if not dealt with properly, severe pipeline pressure transients can degrade the performance of the leak detection system. One of the common basic methods of reducing the effect of pressure transients is using moving averaging windows; having looser thresholds on the shorter averaging windows, while maintaining tighter thresholds on the longer ones. The thresholds are typically set to meet the API 1149 [2] curve for the pipeline. While the post-processing of filtered data and alarm assessment has been explored via different methods such as sequential probability ratio test, to the authors’ knowledge, there is currently no systematic way of selecting the averaging windows to minimize false alarms prior to the post-processing of the average-filtered data. Moreover, to be able to maintain tight thresholds, especially in shorter averaging windows, one of the common methods is to apply dynamic thresholds, i.e. temporarily expanding thresholds when transients occur. While effective in some scenarios, the main disadvantage of this method is that the imbalance caused by a transient may not clear until the entire averaging window period is passed. This causes either extended periods of degraded performance, or more false positives. This paper utilizes an alarming hold time (also referred to as alarm persistence [3]) to remedy this problem where the averaging window length is reduced while maintaining detection time and sensitivity. To find the optimal set of threshold values, hold times, and averaging window lengths, a Particle Swarm Optimization (PSO) is performed. The ‘fitness function’ of the optimization algorithm is designed to minimize total spill volume for leak scenarios and have minimum false alarms for no-leak scenarios. The former is achieved via setting the objective function to the spill volume and the latter is enforced via applying constraints to the optimization algorithm. For no-leak scenarios, the historical operational data of a pipeline system is used. For leak scenarios, the historical data is modified by introducing a bias in the inlet volume of the section to simulate a leak. The result of the PSO provides a set of alarming parameters, threshold value, averaging window length, alarm hold time, and clearing threshold that provide the minimum false alarm rate and spill volume for different detectability ranges. The optimization method proposed in this paper can be applied to any mass or volume balance-based leak detection system that utilizes moving averaging windows. However, the leak detection parameters found with this method depend on the pipeline system.
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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 | 0.001 |
| 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".