dc.contributor.author | Tabella, Gianluca | |
dc.contributor.author | Ciuonzo, Domenico | |
dc.contributor.author | Paltrinieri, Nicola | |
dc.contributor.author | Salvo Rossi, Pierluigi | |
dc.date.accessioned | 2022-03-09T13:00:12Z | |
dc.date.available | 2022-03-09T13:00:12Z | |
dc.date.created | 2022-01-13T12:41:17Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | 24th International Conference on Information Fusion (FUSION) | en_US |
dc.identifier.isbn | 978-1-7377497-1-4 | |
dc.identifier.uri | https://hdl.handle.net/11250/2984049 | |
dc.description.abstract | In this work, we present a spatio-temporal decision fusion approach aimed at performing quickest detection of faults within an Oil and Gas subsea production system. Specifically, a sensor network collectively monitors the state of different pieces of equipment and reports the collected decisions to a fusion center. Therein, a spatial aggregation is performed and a global decision is taken. Such decisions are then aggregated in time by a post-processing center, which performs quickest detection of system fault according to a Bayesian criterion which exploits change-time statistical distributions originated by system components’ datasheets. The performance of our approach is analyzed in terms of both detection- and reliability-focused metrics, with a focus on (fast & inspection-cost-limited) leak detection in a real-world oil platform located in the Barents Sea. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 24th International Conference on Information Fusion (FUSION) | |
dc.title | Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario | en_US |
dc.type | Chapter | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | acceptedVersion | en_US |
dc.identifier.cristin | 1980387 | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |