Skip to main navigation Skip to search Skip to main content

Fault diagnosis of the continuous stirred tank heater using fuzzy-possibilistic c-means algorithm

  • Harbin Institute of Technology

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

Abstract

This paper mainly introduces a practical algorithm called fuzzy-possibilistic c-means (FPCM) clustering algorithm. It is based on fuzzy c-means (FCM) clustering algorithm and possibilistic c-means (PCM) clustering algorithm. FPCM algorithm figures out the existing problems of the above two algorithms and produces both memberships and possibilities simultaneously. For example, FPCM algorithm works out the inconsistency problem of FCM algorithm and overcomes the coincident clusters problem of PCM algorithm. Then this paper applies FPCM algorithm to the fault detection and diagnosis of the continuous stirred tank heaterCSTH). The effect of the fault diagnosis approach is demonstrated on the CSTH benchmark.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2445-2450
Number of pages6
ISBN (Print)9781479923991
DOIs
StatePublished - 2014
Event2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014 - Istanbul, Turkey
Duration: 1 Jun 20144 Jun 2014

Publication series

NameIEEE International Symposium on Industrial Electronics

Conference

Conference2014 IEEE 23rd International Symposium on Industrial Electronics, ISIE 2014
Country/TerritoryTurkey
CityIstanbul
Period1/06/144/06/14

Keywords

  • Clustering
  • Data-driven
  • FPCM
  • Fault detection
  • Fault diagnosis

Fingerprint

Dive into the research topics of 'Fault diagnosis of the continuous stirred tank heater using fuzzy-possibilistic c-means algorithm'. Together they form a unique fingerprint.

Cite this