Possibilistic Bayes Modelling for Predictive Analytics

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Christer Carlsson, Markku Heikkilä, Jozsef Mezei
Publiceringsår: 2014
Förläggare: IEEE Computer Society Institute of Electrical and Electronic Engineers
Moderpublikationens namn: CINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics
ISBN: 978-1-4799-5337-0


Studies in the process industry (and also common sense) show that the most cost effective way to keep production processes running is through predictive maintenance,i.e. to carry out optimal maintenance actions just in time before a process fails. Modern processes are highly auto mated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from very large sets of data. Modern analytics develops algorithms that are fast and effective enough to create possibilities for optimal JIT (Just-in Time) maintenance decisions.

Senast uppdaterad 2020-29-01 vid 08:50