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Notice bibliographique
Résumé
Proponents of new pipeline projects are often asked by regulators to provide estimates of risk and reliability for their proposed pipeline. On existing pipelines, the availability of operating and assessment data is generally considered to be essential to the task of performing an accurate and defendable risk or reliability assessment. For proposed or new pipelines, the absence of these data presents a significant challenge to those performing the analysis. The reliance on industry incident data presents problems, since the vast majority of loss-of-containment incidents relate to older pipelines in which the design, routing criteria, material properties, material manufacturing processes, and early operating practices differ significantly from those that are characteristic of modern pipelines. As a consequence, much of the available failure incident data does not accurately reflect the threats or the magnitudes of the threats that are associated with modern pipelines. In order to address this problem, ‘adjustment factors’ are often applied against incident data to try to account for threat differences between the source data and the intended application. The selection of these adjustment factors can often be quite subjective, however, and open to judgment; therefore, they can be difficult to justify. With the rapidly growing practice of regular in-line inspection (ILI) on transmission pipelines, an extensive repository of ILI data has been accumulated — much of it relating to modern pipelines. Through the judicious selection of source data, ILI data sets can be mined so that an analogue data set can be created that constitutes a reasonable representation of the attributes of reliability of a specific new pipeline of interest. Key reliability properties, such as tool error distribution, feature incidence rate, feature size distribution, and apparent feature growth rate distribution can be derived from such analogue data. By applying these reliability properties in an analysis along with known pipeline design and material properties and their associated distributions, and by taking consideration of planned inspection intervals, a reliability basis can be derived for estimating pipeline risk and reliability. Estimates of risk and reliability that are derived in this manner employ methodologies that are repeatable, defendable, transparent, and free of subjectivity. This paper outlines an approach for completing risk and reliability estimates on new pipelines, and presents the results of some sample calculations. The reliability estimates illustrated are based on an approach whereby corrosion feature size and growth rates are obtained from analogue ILI datasets, and treated as random variables. In that regard, they constitute the probability of exceeding a limit state that represents an approximation of the condition for failure.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle