Publication:
Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time

dc.contributor.authorKhodabakhsh, Athar
dc.contributor.authorArı, İsmail
dc.contributor.authorBakır, M.
dc.contributor.authorErcan, Ali Özer
dc.contributor.departmentElectrical & Electronics Engineering
dc.contributor.departmentComputer Science
dc.contributor.ozuauthorARI, Ismail
dc.contributor.ozuauthorERCAN, Ali Özer
dc.contributor.ozugradstudentKhodabakhsh, Athar
dc.date.accessioned2019-03-27T12:34:48Z
dc.date.available2019-03-27T12:34:48Z
dc.date.issued2018
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.
dc.description.sponsorshipTurkish Petroleum Refineries Inc. (TUPRAS) RD Center
dc.description.versionPublisher version
dc.identifier.doi10.1109/ACCESS.2018.2877097
dc.identifier.endpage64405
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85055149153
dc.identifier.startpage64389
dc.identifier.urihttp://hdl.handle.net/10679/6236
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2018.2877097
dc.identifier.volume6
dc.identifier.wos000451371000001
dc.language.isoeng
dc.peerreviewedyes
dc.publicationstatusPublished
dc.publisherIEEE
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryInternational Refereed Journal
dc.rightsopenAccess
dc.subject.keywordsComplex event processing
dc.subject.keywordsGross error classification
dc.subject.keywordsGross error detection
dc.subject.keywordsOil refinery
dc.subject.keywordsSensor data
dc.subject.keywordsStream data
dc.subject.keywordsSystem behavior
dc.titleMultivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time
dc.typearticle
dspace.entity.typePublication
relation.isOrgUnitOfPublication7b58c5c4-dccc-40a3-aaf2-9b209113b763
relation.isOrgUnitOfPublication85662e71-2a61-492a-b407-df4d38ab90d7
relation.isOrgUnitOfPublication.latestForDiscovery7b58c5c4-dccc-40a3-aaf2-9b209113b763

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