Fuzzy Entropy Used for Predictive Analytics

Research output: Chapter in Book/Conference proceedingChapterScientificpeer-review

7 Citations (Scopus)

Abstract

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.

Original languageUndefined/Unknown
Title of host publicationFuzzy Logic in Its 50th Year
EditorsCengiz Kahraman, Uzay Kaymak, Adnan Yazici
PublisherSpringer, Cham
Pages187–209
ISBN (Electronic)978-3-319-31093-0
ISBN (Print)978-3-319-31091-6
DOIs
Publication statusPublished - 2016
MoE publication typeA3 Part of a book or another research book

Keywords

  • Failure prediction
  • Feature selection
  • Fuzzy entropy
  • Predictive analytics

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