Publication
Modeling system based on machine learning approaches for predictive maintenance applications
| dc.contributor.author | Martins, João Pedro Serrasqueiro | |
| dc.contributor.author | Rodrigues, Filipe Martins | |
| dc.contributor.author | Henriques, Nuno | |
| dc.date.accessioned | 2020-06-12T11:17:58Z | |
| dc.date.available | 2020-06-12T11:17:58Z | |
| dc.date.issued | 2020-06-02 | |
| dc.description.abstract | Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | MARTINS, João Pedro Serrasqueiro; RODRIGUES, Filipe Martins; HENRIQUES, Nuno Paulo Ferreira – Modeling system based on machine learning approaches for predictive maintenance applications. KnE Engineering. ISSN 2518-6841. Vol. 2020 (2020), pp. 857-871 | pt_PT |
| dc.identifier.doi | 10.18502/keg.v5i6.7105 | pt_PT |
| dc.identifier.issn | 2518-6841 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/11834 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Knowledge E | pt_PT |
| dc.subject | Industry 4.0 | pt_PT |
| dc.subject | Artificial intelligence | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Predictive maintenance | pt_PT |
| dc.subject | Prescriptive maintenance | pt_PT |
| dc.title | Modeling system based on machine learning approaches for predictive maintenance applications | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 871 | pt_PT |
| oaire.citation.startPage | 857 | pt_PT |
| oaire.citation.title | KnE Engineering | pt_PT |
| oaire.citation.volume | 2020 | pt_PT |
| person.familyName | Henriques | |
| person.givenName | Nuno | |
| person.identifier.orcid | 0000-0003-4994-7636 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 43f27f6d-58cc-43e1-bd61-8aed89aaa838 | |
| relation.isAuthorOfPublication.latestForDiscovery | 43f27f6d-58cc-43e1-bd61-8aed89aaa838 |
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