001439387 000__ 03249cam\a2200601\i\4500
001439387 001__ 1439387
001439387 003__ OCoLC
001439387 005__ 20230309004426.0
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001439387 007__ cr\cn\nnnunnun
001439387 008__ 210902s2021\\\\si\a\\\\o\\\\\001\0\eng\d
001439387 019__ $$a1266219880
001439387 020__ $$a9789811629402$$q(electronic bk.)
001439387 020__ $$a9811629404$$q(electronic bk.)
001439387 020__ $$z9789811629396$$q(print)
001439387 020__ $$z9811629390
001439387 0247_ $$a10.1007/978-981-16-2940-2$$2doi
001439387 035__ $$aSP(OCoLC)1266386365
001439387 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dOCLCO$$dEBLCP$$dOCLCF$$dN$T$$dUKAHL$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ
001439387 049__ $$aISEA
001439387 050_4 $$aTS192
001439387 08204 $$a658.2/02$$223
001439387 24500 $$aPredictive maintenance in smart factories :$$barchitectures, methodologies, and use-cases /$$cTania Cerquitelli, Nikolaos Nikolakis, Niamh O'Mahony, Enrico Macii, Massimo Ippolito, Sotirios Makris, editors.
001439387 264_1 $$aSingapore :$$bSpringer,$$c2021.
001439387 300__ $$a1 online resource (xiv, 234 pages) :$$billustrations (some color)
001439387 336__ $$atext$$btxt$$2rdacontent
001439387 337__ $$acomputer$$bc$$2rdamedia
001439387 338__ $$aonline resource$$bcr$$2rdacarrier
001439387 4901_ $$aInformation fusion and data science,$$x2510-1536
001439387 500__ $$aIncludes index.
001439387 5050_ $$a1. Industrial digitisation and maintenance: Present and Future -- 2. A hybrid cloud-to-edge predictive maintenance platform -- 3. Data-driven predictive maintenance: a methodology primer -- 4. Services to facilitate predictive maintenance in Industry4.0 -- 5. Predictive analytics in robotic industry.
001439387 506__ $$aAccess limited to authorized users.
001439387 520__ $$aThis book presents the outcome of the European project "SERENA", involving fourteen partners as international academics, technological companies, and industrial factories, addressing the design and development of a plug-n-play end-to-end cloud architecture, and enabling predictive maintenance of industrial equipment to be easily exploitable by small and medium manufacturing companies with a very limited data analytics experience. Perspectives and new opportunities to address open issues on predictive maintenance conclude the book with some interesting suggestions of future research directions to continue the growth of the manufacturing intelligence
001439387 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 2, 2021).
001439387 650_0 $$aPlant maintenance$$xTechnological innovations.
001439387 650_0 $$aPredictive control.
001439387 650_6 $$aUsines$$xEntretien$$xInnovations.
001439387 650_6 $$aCommande prédictive.
001439387 655_0 $$aElectronic books.
001439387 7001_ $$aCerquitelli, Tania,$$eeditor.
001439387 7001_ $$aNikolakis, Nikolaos,$$eeditor.
001439387 7001_ $$aO'Mahony, Niamh,$$eeditor.
001439387 7001_ $$aMacii, Enrico,$$eeditor.
001439387 7001_ $$aIppolito, Massimo,$$eeditor.
001439387 7001_ $$aMakris, Sotirios,$$eeditor.
001439387 77608 $$iPrint version:$$z9811629390$$z9789811629396$$w(OCoLC)1247842112
001439387 830_0 $$aInformation fusion and data science,$$x2510-1536
001439387 852__ $$bebk
001439387 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-2940-2$$zOnline Access$$91397441.1
001439387 909CO $$ooai:library.usi.edu:1439387$$pGLOBAL_SET
001439387 980__ $$aBIB
001439387 980__ $$aEBOOK
001439387 982__ $$aEbook
001439387 983__ $$aOnline
001439387 994__ $$a92$$bISE