IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency  

Public ISBD Titre : IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future Type de document : document électronique Auteurs : Oliver Niggemann, Editeur scientifique ; Peter Schüller, Editeur scientifique Editeur : Berlin [Germany] : Springer Nature Limited Année de publication : 2018 Importance : 129 p. Présentation : ill. ISBN/ISSN/EAN : 978-3-662-57805-6 Langues : Anglais (eng) Tags : Industrial automation  Cyber-physical systems  Predictive maintenance  Digital twins  Machine learning  Production optimization  Smart manufacturing  Process modelling  Virtual commissioning  Factory of the Future Index. décimale : 670.427 Mécanisation et automatisation des techniques de fabrication Résumé : This open-access work presents the core findings of the European research project IMPROVE, which focuses on integrating machine learning into industrial environments to create intelligent Cyber-Physical Systems (CPS). By leveraging data-driven solutions, the research enhances machine reliability and operational efficiency across four critical domains: simulation and optimization for flexible production, automated condition monitoring to streamline maintenance, advanced alarm management to identify root causes of system failures, and quality prediction to proactively detect manufacturing defects. Ultimately, the project demonstrates how self-learning technologies can significantly boost Overall Equipment Effectiveness (OEE) and support human operators, providing a scalable framework for more resilient and sustainable Industry 4.0 operations. En ligne : https://doi.org/10.1007/978-3-662-57805-6 IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : Intelligent Methods for the Factory of the Future [document électronique] / Oliver Niggemann, Editeur scientifique ; Peter Schüller, Editeur scientifique . - Berlin (Germany) : Springer Nature Limited, 2018 . - 129 p. : ill. ISBN : 978-3-662-57805-6 Langues : Anglais (eng) Tags : Industrial automation  Cyber-physical systems  Predictive maintenance  Digital twins  Machine learning  Production optimization  Smart manufacturing  Process modelling  Virtual commissioning  Factory of the Future Index. décimale : 670.427 Mécanisation et automatisation des techniques de fabrication Résumé : This open-access work presents the core findings of the European research project IMPROVE, which focuses on integrating machine learning into industrial environments to create intelligent Cyber-Physical Systems (CPS). By leveraging data-driven solutions, the research enhances machine reliability and operational efficiency across four critical domains: simulation and optimization for flexible production, automated condition monitoring to streamline maintenance, advanced alarm management to identify root causes of system failures, and quality prediction to proactively detect manufacturing defects. Ultimately, the project demonstrates how self-learning technologies can significantly boost Overall Equipment Effectiveness (OEE) and support human operators, providing a scalable framework for more resilient and sustainable Industry 4.0 operations. En ligne : https://doi.org/10.1007/978-3-662-57805-6

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
9783662578056	670.427 NIG	E-Book	En Ligne	Technologie (sciences appliquées)	Disponible

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