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dc.contributor.authorEcheverria, Lluis
dc.contributor.authorBonada, Francesc
dc.contributor.authorAnzaldi, Gabriel
dc.contributor.authorDomingo, Xavier
dc.date.accessioned2021-06-02T10:13:14Z
dc.date.available2021-06-02T10:13:14Z
dc.date.issued2020
dc.identifierONIX_20210602_10.5772/intechopen.89967_475
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/49361
dc.description.abstractIndustry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::K Economics, finance, business & management::KN Industry & industrial studies
dc.subject.othermachine learning, supervised learning, unsupervised learning, classification, regression, ensembles, artificial intelligence, data mining, data-driven, industry 4.0, smart manufacturing, cyber-physical systems, predictive analytics
dc.titleChapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry
dc.typechapter
oapen.identifier.doi10.5772/intechopen.89967
oapen.relation.isPublishedBy09f6769d-48ed-467d-b150-4cf2680656a1
oapen.relation.isFundedByFP7-2012-NMP-ICT-FoF
oapen.grant.number314581
oapen.grant.acronymDES-MOLD


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