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Predictive maintenance in tomorrow's industries

Redaktionell betreuter Blog-Beitrag auf https://semiengineering.com, 09.08.2018
 
: Enge-Rosenblatt, Olaf; Brandt, Steven

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Volltext (HTML; )

2018
Englisch
Elektronische Publikation
Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) ()

Abstract
Combining data analytics and process knowledge to predict machine failures in advance: Tomorrow’s production plants must be efficient and adaptive, which is the key to survival in modern, highly competitive markets brought about by digitalization and automation. Future-oriented companies are increasingly focusing on a tight combination of automation and computer technology as promised by the Industry 4.0 paradigm. More and more globally distributed technical systems facilitate exchange of data and remote analysis, e.g. in superordinate IT infrastructures like in a cloud. Local and cloud-based data analysis can be used to realize comprehensive predictive maintenance solutions in order to optimize maintenance intervals, maximize machine lifetime and reduce expensive machine downtime. Well-prepared, successful predictive maintenance solutions not only require knowledge about mathematical statistics and classification methods but also about the complete production process.

: http://publica.fraunhofer.de/dokumente/N-509979.html