dc.contributor.author | Tabella, Gianluca | |
dc.contributor.author | Ciuonzo, Domenico | |
dc.contributor.author | Paltrinieri, Nicola | |
dc.contributor.author | Salvo Rossi, Pierluigi | |
dc.date.accessioned | 2022-03-31T08:19:20Z | |
dc.date.available | 2022-03-31T08:19:20Z | |
dc.date.created | 2022-01-13T12:41:17Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-1-7377497-1-4 | |
dc.identifier.uri | https://hdl.handle.net/11250/2988729 | |
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 | Institute of Electrical and Electronics Engineers (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.description.version | acceptedVersion | en_US |
dc.rights.holder | © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.identifier.doi | 10.23919/FUSION49465.2021.9626941 | |
dc.identifier.cristin | 1980387 | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.fulltext | postprint | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |