Analytical Workbench: A Framework to Support Predictive Maintenance of Industrial Systems

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dc.contributor.authorChrysostomo G.G.C.
dc.contributor.authorVallim M.V.B.A.
dc.contributor.authorSilva L.A.
dc.contributor.authorFilho A.R.A.V.
dc.date.accessioned2024-03-12T23:46:34Z
dc.date.available2024-03-12T23:46:34Z
dc.date.issued2020
dc.description.abstract© 2020 IEEE.This work proposes a framework called Analytical workbench that aims to support the decision making of power generation systems. The framework is structured in three modules. An operational module which receives operating data and prepares it for analysis purposes. The tactical module which allows real time system monitoring. Finally, the strategic module, which allows to make inferences about the future state of the plant's operating data. The results can be seen in a real case study in a Brazilian hydroelectric plant and the main highlights are: identification of faulty sensors, measurement errors, real-time monitoring (every 5 seconds) of all data or just some selected variables and, finally, forecast of the plant's operational status in one more day.
dc.description.firstpage275
dc.description.lastpage283
dc.identifier.doi10.1109/CLEI52000.2020.00039
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34902
dc.relation.ispartofProceedings - 2020 46th Latin American Computing Conference, CLEI 2020
dc.rightsAcesso Restrito
dc.subject.otherlanguageinternet of things
dc.subject.otherlanguagepredictive maintenance
dc.subject.otherlanguagesmart factory
dc.subject.otherlanguagesupervisory systems
dc.titleAnalytical Workbench: A Framework to Support Predictive Maintenance of Industrial Systems
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85113601746
local.scopus.subjectFaulty sensor
local.scopus.subjectHydroelectric plant
local.scopus.subjectIndustrial systems
local.scopus.subjectOperating data
local.scopus.subjectPower generation systems
local.scopus.subjectReal case
local.scopus.subjectReal time monitoring
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113601746&origin=inward
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