Aware online interdependency modelling via evidence theory
| Autor: | Gabriele Oliva, Chiara Foglietta, Stefano Panzieri, Giusj Digioia |
|---|---|
| Přispěvatelé: | Digioia, G, Foglietta, Chiara, Oliva, G, Panzieri, Stefano |
| Rok vydání: | 2013 |
| Předmět: |
Engineering
Situation awareness business.industry Management science Specific-information media_common.quotation_subject interdependency modelling Context (language use) Transferable belief model Critical infrastructure Interdependence evidence theory General Energy critical infrastructure Risk analysis (engineering) Order (exchange) Safety Risk Reliability and Quality business Representation (mathematics) General Environmental Science media_common |
| Zdroj: | International Journal of Critical Infrastructures. 9:74 |
| ISSN: | 1741-8038 1475-3219 |
| DOI: | 10.1504/ijcis.2013.051604 |
| Popis: | "Critical infrastructure interdependency models are typically used in a simulation-based perspective, in order to perform 'what if?' analyses and identify structural vulnerabilities in a dynamic perspective. While in the literature some attempts have been made to use interdependency models at real time, such approaches are flawed by the inability to properly determine the ongoing situation. Such models, typically, receive data from SCADA systems, which are mostly able to assess the effects of failures rather than the causes, while knowing the typology of failure would increase dramatically the predictive-ability of online interdependency models. In this paper, a situation awareness framework is provided with the aim to complement online interdependency models by providing more specific information on the causes of the outages highlighted by sensor data. In order to determine such causes, in this paper a transferable belief model representation is adopted to increase the awareness of interdependency models on fault causes. Moreover in this paper some of the limitations of evidence theory methods are highlighted and discussed, with particular reference to a real time context, providing some insights on how to overcome them, especially the closed-world assumption." |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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