Fuzzy Entropy Used for Predictive Analytics

A3 Book section, Chapters in research books

Internal Authors/Editors

Publication Details

List of Authors: Christer Carlsson, Markku Heikkilä, József Mezei
Editors: Cengiz Kahraman, Uzay Kaymak, Adnan Yazici
Place: Cham
Publication year: 2016
Publisher: Springer, Cham
Book title: Fuzzy Logic in Its 50th Year
Title of series: Studies on fuzziness and soft computing
Volume number: 341
Start page: 187
End page: 209
ISBN: 978-3-319-31091-6
eISBN: 978-3-319-31093-0


Process interruptions in (very) large production systems are difficult
to deal with. Modern processes are highly automated; data is collected
with sensor technology that forms a big data context and offers
challenges to identify coming failures from the very large sets of data.
The sensors collect huge amounts of data but the failure events are few
and infrequent and hard to find (and even harder to predict). In this
article, our goal is to develop models for predictive maintenance in a
big data environment. The purpose of feature selection in the context of
predictive maintenance is to identify a small set of process
diagnostics that are sufficient to predict future failures. We apply
interval-valued fuzzy sets and various entropy measures defined on them
to perform feature selection on process diagnostics. We show how these
models can be utilized as the basis of decision support systems in
process industries to aid predictive maintenance.


Failure prediction, Feature selection, Fuzzy entropy, Predictive analytics

Last updated on 2020-05-04 at 05:30