Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario

• Main Authors: Tabella G., Ciuonzo D., Paltrinieri N., Rossi P. S.
• Other Authors: Tabella, G., Ciuonzo, D., Paltrinieri, N., Rossi, P. S.
• Format: Conference Object
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: Data fusion; Distributed detection; Maintenance; Monitoring; Reliability; Wireless sensor network; Barents Sea
• Online Access: http://hdl.handle.net/11588/873271

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.