ELFpm: an ensemble-based learning framework for predictive maintenance in industry 4.0
| Autor: | Dalzochio, Jovani |
|---|---|
| Přispěvatelé: | Barbosa, Jorge Luis Victória, Kunst, Rafael |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Repositório Institucional da UNISINOS (RBDU Repositório Digital da Biblioteca da Unisinos) Universidade do Vale do Rio dos Sinos (UNISINOS) instacron:UNISINOS |
| Popis: | Submitted by Maicon Juliano Schmidt (maicons) on 2020-08-06T20:21:52Z No. of bitstreams: 1 Jovani Dalzochio_.pdf: 3688590 bytes, checksum: 5b32ab959191b378b32532ff47077652 (MD5) Made available in DSpace on 2020-08-06T20:21:52Z (GMT). No. of bitstreams: 1 Jovani Dalzochio_.pdf: 3688590 bytes, checksum: 5b32ab959191b378b32532ff47077652 (MD5) Previous issue date: 2020-03-17 CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior The topic of predictive maintenance has great relevance in the search for the rationalization and efficiency of the industrial plants in the context of Industry 4.0. Monitoring equipment parameters and identifying behavior changes that identify a future failure allows for anticipation of maintenance while avoiding unnecessary preventive maintenance. There are numerous works in the literature that work towards the prediction of maintenance of various equipment. However, the same equipment has different behavior depending on the conditions of use or the operating environment, making a tool capable of being trained for new environments is necessary. This work describes the methodology of creating a framework that can be configured to work on predicting equipment failures, that is, regardless of location or condition of use. For this, starting from the initial configuration of the framework, the use of an ontology is applied in the choice of the best prediction technique for each established condition of the initial parameterization. |
| Databáze: | OpenAIRE |
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