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dc.contributor.authorArato, Adyles [UNESP]
dc.contributor.authorAlmeida, Fabrício [UNESP]
dc.date.accessioned2022-04-28T19:03:00Z
dc.date.available2022-04-28T19:03:00Z
dc.date.issued2008-01-01
dc.identifier.citation7th European Conference on Structural Dynamics, EURODYN 2008.
dc.identifier.urihttp://hdl.handle.net/11449/220571
dc.description.abstractCurrently, a number of researchers have been working to understand the health monitoring and damage detection problems. Structural health monitoring (SHM) and damage detection techniques are instrumental for the engineering community both for safety and cost effectiveness reasons. The project herein demonstrates that maintenance can be planned before a fault occurs, minimizing thus serious damages probability. A customized conditional maintenance design has been developed by means of SHM and damage techniques. Such a system provides fixed bands as well as trend graphics which estimate a possible fault alarm, emergency time and detection damage as well. The artificial neural network theory has been the tool used for its fast detecting and determining damages on an operating machine before critical conditions, which leads to an optimized maintenance and production management.en
dc.language.isoeng
dc.relation.ispartof7th European Conference on Structural Dynamics, EURODYN 2008
dc.sourceScopus
dc.subjectCondition monitoring
dc.subjectMaintenance management
dc.subjectNeural network
dc.subjectVibration
dc.titleConditional maintenance using artificial neural network and vibration techniques to improve production cost-effectivenessen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationLaboratory of Vibration and Instrumentation (LVI) Universidade Estadual Paulista (UNESP) Faculdade de Engenharia de Ilha Solteira, Av. Brazil 56
dc.description.affiliationUnespLaboratory of Vibration and Instrumentation (LVI) Universidade Estadual Paulista (UNESP) Faculdade de Engenharia de Ilha Solteira, Av. Brazil 56
dc.identifier.scopus2-s2.0-84959861165
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