Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry
Industry 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 moni...
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| Materiálatiipa: | Online |
| Giella: | eaŋgalasgiella |
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InTechOpen
2021
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| Fáttát: | |
| Liŋkkat: | ONIX_20210602_10.5772/intechopen.89967_475 |
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| _version_ | 1869529762889728000 |
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| author | Echeverria, Lluis Bonada, Francesc Anzaldi, Gabriel Domingo, Xavier |
| author_browse | Anzaldi, Gabriel Bonada, Francesc Domingo, Xavier Echeverria, Lluis |
| author_facet | Echeverria, Lluis Bonada, Francesc Anzaldi, Gabriel Domingo, Xavier |
| author_sort | Echeverria, Lluis |
| collection | Directory of Open Access Books |
| description | Industry 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. |
| format | Online |
| id | doab-20.500.12854ir-70415 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-704152025-01-21T22:54:28Z Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry Echeverria, Lluis Bonada, Francesc Anzaldi, Gabriel Domingo, Xavier machine learning, supervised learning, unsupervised learning, classification, regression, ensembles, artificial intelligence, data mining, data-driven, industry 4.0, smart manufacturing, cyber-physical systems, predictive analytics thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies Industry 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. 2021-06-02T10:13:14Z 2020 chapter ONIX_20210602_10.5772/intechopen.89967_475 https://library.oapen.org/handle/20.500.12657/49361 https://directory.doabooks.org/handle/20.500.12854/70415 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/49361/1/69975.pdf https://library.oapen.org/bitstream/20.500.12657/49361/1/69975.pdf https://library.oapen.org/bitstream/20.500.12657/49361/1/69975.pdf InTechOpen 10.5772/intechopen.89967 10.5772/intechopen.89967 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access |
| spellingShingle | machine learning, supervised learning, unsupervised learning, classification, regression, ensembles, artificial intelligence, data mining, data-driven, industry 4.0, smart manufacturing, cyber-physical systems, predictive analytics thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies Echeverria, Lluis Bonada, Francesc Anzaldi, Gabriel Domingo, Xavier Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry |
| title | Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry |
| title_full | Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry |
| title_fullStr | Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry |
| title_full_unstemmed | Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry |
| title_short | Chapter AI for Improving the Overall Equipment Efficiency in Manufacturing Industry |
| title_sort | chapter ai for improving the overall equipment efficiency in manufacturing industry |
| topic | machine learning, supervised learning, unsupervised learning, classification, regression, ensembles, artificial intelligence, data mining, data-driven, industry 4.0, smart manufacturing, cyber-physical systems, predictive analytics thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies |
| topic_facet | machine learning, supervised learning, unsupervised learning, classification, regression, ensembles, artificial intelligence, data mining, data-driven, industry 4.0, smart manufacturing, cyber-physical systems, predictive analytics thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies |
| url | ONIX_20210602_10.5772/intechopen.89967_475 |
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