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Possibilistic Bayes Modelling for Predictive Analytics

Christer Carlsson, Markku Heikkilä, Jozsef Mezei, Possibilistic Bayes Modelling for Predictive Analytics. In: IEEE International Symposium on Computational Intelligence and Informatics, 15–20, IEEE, 2014.

http://dx.doi.org/10.1109/CINTI.2014.7028671

Abstract:

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.

BibTeX entry:

@INPROCEEDINGS{aconv21986,
  title = {Possibilistic Bayes Modelling for Predictive Analytics},
  booktitle = {IEEE International Symposium on Computational Intelligence and Informatics},
  author = {Carlsson, Christer and Heikkilä, Markku and Mezei, Jozsef},
  publisher = {IEEE},
  pages = {15–20},
  year = {2014},
}

Belongs to TUCS Research Unit(s): Institute for Advanced Management Systems Research (IAMSR)

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