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Decentralized data analytics for maintenance in industrie 4.0

 
: Uhlmann, Eckart; Laghmouchi, Abdelhakim; Geisert, Claudio; Hohwieler, Eckhard

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Procedia manufacturing 11 (2017), S.1120-1126
ISSN: 2351-9789
International Conference on Flexible Automation and Intelligent Manufacturing (FAIM) <27, 2017, Modena>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag
Fraunhofer IPK ()

Abstract
Due to the increased digital networking of machines and systems in the production area, large datasets are generated. In addition, more external sensors are installed at production systems to acquire data for production and maintenance optimization purposes. Therefore, data analytics and interpretation is one of the challenges in Industrie 4.0 applications. Reliable analysis of data (e.g. internal and external sensors), such as system-internal alarms and messages produced during the operation, can be used to optimize production and maintenance processes. Furthermore, information and knowledge can be extracted from raw data and used to develop data-driven business models and services, e.g. offer new availability contracts for production systems. This paper illustrates an approach for decentralized data analytics based on smart sensor networks.

: http://publica.fraunhofer.de/dokumente/N-471029.html