Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/100622
DC Field | Value | Language |
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dc.contributor.author | Mateus, Balduíno César | - |
dc.contributor.author | Mendes, Mateus | - |
dc.contributor.author | Farinha, José Torres | - |
dc.contributor.author | Cardoso, António Marques | - |
dc.date.accessioned | 2022-07-07T11:37:50Z | - |
dc.date.available | 2022-07-07T11:37:50Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2076-3417 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/100622 | - |
dc.description.abstract | Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model. | pt |
dc.language.iso | eng | pt |
dc.relation | POCI-01-0145-FEDER-029494 | pt |
dc.relation | Marie Sklodowvska-Curie grant agreement 871284 project SSHARE | pt |
dc.relation | PTDC/EEI-EEE/29494/2017 | pt |
dc.relation | UIDB/04131/2020 | pt |
dc.relation | UIDP/04131/2020 | pt |
dc.relation | Project 01/SAICT/2016 nº 022153 | pt |
dc.relation | UIDB/00285/2020 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | time series prediction | pt |
dc.subject | LSTM prediction | pt |
dc.subject | deep learning prediction | pt |
dc.subject | predictive maintenance | pt |
dc.title | Anticipating Future Behavior of an Industrial Press Using LSTM Networks | pt |
dc.type | article | - |
degois.publication.firstPage | 6101 | pt |
degois.publication.issue | 13 | pt |
degois.publication.title | Applied Sciences (Switzerland) | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/app11136101 | pt |
degois.publication.volume | 11 | pt |
dc.date.embargo | 2021-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.project.grantno | Electromechatronic Systems Research Centre - CISE | - |
crisitem.project.grantno | Electromechatronic Systems Research Centre | - |
crisitem.project.grantno | Centre for Mechanical Enginnering, Materials and Processes | - |
crisitem.author.researchunit | CEMMPRE - Centre for Mechanical Engineering, Materials and Processes | - |
crisitem.author.orcid | 0000-0002-9694-8079 | - |
Appears in Collections: | I&D ISR - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Anticipating-future-behavior-of-an-industrial-press-using-lstm-networksApplied-Sciences-Switzerland.pdf | 639.01 kB | Adobe PDF | View/Open |
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