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dc.contributor.authorKhodabakhsh, Athar
dc.contributor.authorArı, İsmail
dc.contributor.authorBakır, M.
dc.contributor.authorErcan, Ali Özer
dc.date.accessioned2019-03-27T12:34:48Z
dc.date.available2019-03-27T12:34:48Z
dc.date.issued2018
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10679/6236
dc.identifier.urihttps://ieeexplore.ieee.org/document/8501917
dc.description.abstractLarge-scale oil refineries are equipped with mission-critical heavy machinery (boilers, engines, turbines, and so on) and are continuously monitored by thousands of sensors for process efficiency, environmental safety, and predictive maintenance purposes. However, sensors themselves are also prone to errors and failure. The quality of data received from these sensors should be verified before being used in system modeling. There is a need for reliable methods and systems that can provide data validation and reconciliation in real-time with high accuracy. In this paper, we develop a novel method for real-time data validation, gross error detection and classification over multivariate sensor data streams. The validated and high-quality data obtained from these processes is used for pattern analysis and modeling of industrial plants. We obtain sensor data from the power and petrochemical plants of an oil refinery and analyze them using various time-series modeling and data mining techniques that we integrate into a complex event processing engine. Next, we study the computational performance implications of the proposed methods and uncover regimes where they are sustainable over fast streams of sensor data. Finally, we detect shifts among steady-states of data, which represent systems' multiple operating modes and identify the time when a model reconstruction is required using DBSCAN clustering algorithm.en_US
dc.description.sponsorshipTurkish Petroleum Refineries Inc. (TUPRAS) RD Center
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Access
dc.rightsopenAccess
dc.titleMultivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-timeen_US
dc.typeArticleen_US
dc.description.versionPublisher versionen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-6159-0484 & YÖK ID 43541) Arı, İsmail
dc.contributor.authorID(ORCID 0000-0003-1126-8259 & YÖK ID 35788) Ercan, Ali
dc.contributor.ozuauthorArı, İsmail
dc.contributor.ozuauthorErcan, Ali Özer
dc.identifier.volume6en_US
dc.identifier.startpage64389en_US
dc.identifier.endpage64405en_US
dc.identifier.wosWOS:000451371000001
dc.identifier.doi10.1109/ACCESS.2018.2877097en_US
dc.subject.keywordsComplex event processingen_US
dc.subject.keywordsGross error classificationen_US
dc.subject.keywordsGross error detectionen_US
dc.subject.keywordsOil refineryen_US
dc.subject.keywordsSensor dataen_US
dc.subject.keywordsStream dataen_US
dc.subject.keywordsSystem behavioren_US
dc.identifier.scopusSCOPUS:2-s2.0-85055149153
dc.contributor.ozugradstudentKhodabakhsh, Athar
dc.contributor.authorMale2
dc.contributor.authorFemale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff and PhD Student


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