A discussion on the use of Eliminative Argumentation (EA) to identify Key Performance Indicators (KPIs) for the CERN LHC Machine Protection System
Notice bibliographique
Résumé
Key Performance Indicators (KPIs) and Safety Performance Indicators (SPIs) form an integral part of the Safety Management System (SMS) for a selected system.They provide a key insight into the system's safety performance and risk management, and enable data-driven decision-making.A KPI for a system is defined as "a quantifiable measure used to evaluate the success of an organization, employee, etc. in meeting objectives for performance".The KPIs discussed within this paper denote a measure of success/performance of the relevant identified sub-systems.Integration of KPIs and SPIs serves as a method of performance and safety evaluation of the systems they are associated with.KPIs can be used to estimate the safety performance of a system, as well as to support the safety case and ensure that it remains "fit for purpose" and "live".The paper also discusses how KPIs can be grouped into "leading" and "lagging" indicators.A leading indicator is one that tracks the occurrence of events that, while not themselves harmful, are expected to precede, or indicate the potential for, more harmful events.A lagging indicator is one that tracks the occurrence rate of hazards and/or loss events, such as crashes, injuries and fatalities.Leading and lagging indicators have limitations, advantages and disadvantages, which will be discussed further in the paper.Further we also discuss the challenges of accurate data collection to support KPIs.KPIs have a variety of potential uses, such as tracking safety trends over time, measuring system compliance to regulations/legislation, and providing evidence for the system's safety case.This paper will focus on how KPIs can be defined from the safety (assurance) case assessment process.Specifically, this paper demonstrates the use of Eliminative Argumentation (EA) to define the potential hazards associated with the machine protection system at the nuclear research facility CERN.We discuss the evaluation and identification of the KPIs for each of these systems.Further, we show how performance indicators are identified with the EA assessment and the corresponding nodes, whilst demonstrating how the content of this assessment is linked via a "golden thread".We show how they can be analysed post-mortem to ensure that the safety case remains valid and "live" as the system changes.Finally, we discuss how the use of KPIs can benefit the safety case and why ensuring that it remains "live" (fit for purpose) is critical to the continued safe operation of a system.In summary, KPIs play a critical role in keeping a safety case live by providing ongoing monitoring, driving continuous improvement, providing documentation, and establishing accountability for safety performance.By using them effectively, organizations can ensure that safety goals are being met over time.
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.
Comment cette classification a été obtenuedéplier
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,001 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».