Dynamic Risk‐Adjusted Monitoring of Time Between Events: Applications of NHPP in Pipeline Accident Surveillance
Bibliographic record
Abstract
ABSTRACT Monitoring time between events (TBE) is essential in many industrial settings. Traditional methods assume a time‐invariant failure rate which is unsuitable for complex systems where the failure mechanism changes over time due to degradation. The Non‐Homogeneous Poisson Process (NHPP) better models these systems by allowing a time‐varying failure intensity. Additionally, failure patterns of such systems are often influenced by risk factors like environmental conditions and human interventions. Restoration of such systems also imposes multiple cost constraints. This work proposes a novel approach: a risk‐adjusted control chart based on the NHPP model, specifically designed for monitoring the ratio, Cost/TBE, called the average cost per time unit. Risk‐adjustment enhances the chart's detectability of shifts in the failure process by isolating the risk factors' contribution. Moreover, incorporating cost‐based monitoring statistics not only emphasizes the cost impacts beside the failures themselves but also improves the chart's interpretability. The effectiveness of the proposed method is demonstrated through a comprehensive numerical study showing its superior performance compared to existing methods. The comparison study shows that ignoring the risk factors in the chart design leads to numerous false alarms, undermining the chart's effectiveness. We also illustrated the proposed method through a case study on monitoring a network of oil pipeline accidents.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 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".