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Collaborative fault diagnosis decision fusion algorithm based on improved DS evidence theory

  • Xiue Gao
  • , Bo Chen
  • , Shifeng Chen
  • , Kesheng Wang
  • , Yi Wang
  • , Wenxue Xie
  • , Jin Yuan
  • , Kristian Martinsen
  • , Tamal Ghosh
  • Lingnan Normal University
  • Norwegian University of Science and Technology
  • University of Plymouth

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

DS evidence theory has in obtaining a correct diagnosis when confronted with highly conflicting evidence, a collaborative fault diagnosis decision fusion algorithm based on an improved version of DS evidence theory is proposed. The algorithm builds upon the closeness of certain kinds of evidence produced by existing DS evidence theory algorithms. According to the importance of the diagnostic information, weights are assigned to reduce the conflicting information while retaining the important diagnostic information. Simulated example shows that the algorithm could reduce the impact of conflicts in diagnostic information and improve the accuracy of the decision fusion process.

Original languageEnglish
Title of host publicationAdvanced Manufacturing and Automation IX (IWAMA 2019)
PublisherSpringer
Chapter47
Pages379-387
DOIs
Publication statusPublished - 3 Jan 2020

Publication series

NameAdvanced Manufacturing and Automation IX
Volume634
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

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