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Predictive disturbance management in manufacturing control systems

dc.contributor.authorLeitão, Paulo
dc.contributor.authorRestivo, Francisco
dc.date.accessioned2010-01-27T20:02:31Z
dc.date.available2010-01-27T20:02:31Z
dc.date.issued2004
dc.description.abstractThe manufacturing systems are dynamic, non-linear and often chaotic environments, subject to the occurrence of unexpected disturbances that leads to deviations from the initial plans and usually degrades the performance of the system. The treatment of exceptions and disturbances is one major requirement to the next generation of intelligent manufacturing control systems, that should be able to treat emergency as a normal situation. In this paper, a predictive disturbance management approach that transforms the traditional “fail and recover” practices into “predict and prevent” practices, improving the control system performance, will be presented. The predictive mechanism is based in the frequency analysis of each type of disturbance to find repetitive patterns in their occurrence.pt
dc.identifier.citationLeitão, Paulo; Restivo, Francisco (2004). Predictive disturbance management in manufacturing control systems. In Proceedings of the IMS International Forum. Como, Italy. p.589-596pt
dc.identifier.urihttp://hdl.handle.net/10198/1546
dc.language.isoengpt
dc.subjectPredictive disturbance managementpt
dc.subjectIntelligent manufacturing systemspt
dc.subjectHolonic manufacturing systemspt
dc.titlePredictive disturbance management in manufacturing control systemspt
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferencePlaceComo, Italypt
oaire.citation.endPage596pt
oaire.citation.startPage589pt
oaire.citation.titleProceedings of the IMS International Forumpt
person.familyNameLeitão
person.givenNamePaulo
person.identifierA-8390-2011
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0002-2151-7944
person.identifier.scopus-author-id35584388900
rcaap.rightsopenAccesspt
rcaap.typeconferenceObjectpt
relation.isAuthorOfPublication68d9eb25-ad4f-439b-aeb2-35e8708644cc
relation.isAuthorOfPublication.latestForDiscovery68d9eb25-ad4f-439b-aeb2-35e8708644cc

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