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A fault diagnosis algorithm of artificial immune network model based on neighborhood rough set theory

  • Yonghuang Zheng
  • , Feng Tian
  • , Renhou Li
  • , Qingsong Song
  • , Longzhuang Li
  • Xi'an Jiaotong University
  • Texas A&M University-Corpus Christi

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

4 Scopus citations

Abstract

This paper proposes a fault diagnosis algorithm of artificial immune network model based on neighborhood rough set theory. In the algorithm, the relationships between pruning threshold, the rates of mis-diagnosis, and missed diagnosis are discussed in the shape space. In addition, the fault mode boundaries, the fault mode inclusion relations, an observation index and an algorithm for adaptively adjusting pruning threshold are described. The simulation experiments show that the proposed fault diagnosis algorithm can identify the unknown and untrained fault modes, while keeping misdiagnosis rate and missed diagnosis rate low.

Original languageEnglish
Title of host publicationProceedings - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012
Pages621-627
Number of pages7
StatePublished - 2012
Event2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012 - Taipei, Taiwan, Province of China
Duration: 23 Oct 201225 Oct 2012

Publication series

NameProceedings - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012

Conference

Conference2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012
Country/TerritoryTaiwan, Province of China
CityTaipei
Period23/10/1225/10/12

Keywords

  • Neighborhood rough set
  • artificial immune network
  • fault diagnosis
  • the pruning threshold adjustment

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