Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/108332
DC Field | Value | Language |
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dc.contributor.author | Reis, Marco S. | - |
dc.contributor.author | Gins, Geert | - |
dc.date.accessioned | 2023-08-24T10:39:51Z | - |
dc.date.available | 2023-08-24T10:39:51Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 2227-9717 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/108332 | - |
dc.description.abstract | We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos. | pt |
dc.language.iso | eng | pt |
dc.publisher | MDPI | pt |
dc.relation | project 016658 (references PTDC/QEQ-EPS/1323/2014, POCI-01-0145-FEDER-016658) financed by Project 3599-PPCDT (Promover a Produção Científica e Desenvolvimento Tecnológico e a Constituição de Redes Temáticas) and co-financed by the European Union’s FEDER | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | industrial process monitoring | pt |
dc.subject | fault detection and diagnosis | pt |
dc.subject | prognosis | pt |
dc.subject | process health | pt |
dc.subject | equipment health | pt |
dc.title | Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis | pt |
dc.type | article | - |
degois.publication.firstPage | 35 | pt |
degois.publication.issue | 3 | pt |
degois.publication.title | Processes | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/pr5030035 | pt |
degois.publication.volume | 5 | pt |
dc.date.embargo | 2017-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.fulltext | Com Texto completo | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.author.researchunit | CIEPQPF – Chemical Process Engineering and Forest Products Research Centre | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.orcid | 0000-0002-4997-8865 | - |
Appears in Collections: | I&D CERES - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Industrial-process-monitoring-in-the-big-dataindustry-40-era-From-detection-to-diagnosis-to-prognosisProcesses.pdf | 772.63 kB | Adobe PDF | View/Open |
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