A framework for big data analytical process and mapping—baprom: Description of an application in an industrial environment

Página do item simplificado
dc.contributor.authorDe Carvalho Chrysostomo G.G.
dc.contributor.authorDe Aguiar Vallim M.V.B.
dc.contributor.authorDa Silva L.S.
dc.contributor.authorSilva L.A.
dc.contributor.authorDe Aguiar Vallim Filho A.R.
dc.date.accessioned2024-03-12T23:46:02Z
dc.date.available2024-03-12T23:46:02Z
dc.date.issued2020
dc.description.abstract© 2020 by the authors. Licensee MDPI, Basel, Switzerland.This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical “workbench” was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures.
dc.description.issuenumber22
dc.description.volume13
dc.identifier.doi10.3390/en13226014
dc.identifier.issn1996-1073
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34872
dc.relation.ispartofEnergies
dc.rightsAcesso Aberto
dc.subject.otherlanguageBig data process
dc.subject.otherlanguageMachine learning
dc.subject.otherlanguagePredictive maintenance
dc.titleA framework for big data analytical process and mapping—baprom: Description of an application in an industrial environment
dc.typeArtigo
local.scopus.citations6
local.scopus.eid2-s2.0-85103344110
local.scopus.subjectAnalytical process
local.scopus.subjectArtificial intelligence algorithms
local.scopus.subjectDecision support tools
local.scopus.subjectExploratory analysis
local.scopus.subjectIndustrial environments
local.scopus.subjectMaintenance decisions
local.scopus.subjectPredictive modeling
local.scopus.subjectTime series forecasting models
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103344110&origin=inward
Arquivos
Coleções
Artigos de periódico