Fault diagnosis with dynamic fuzzy discrete event system approach

Kilic E., Karasu C., Leblebicioglu K.

ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS, pp.117-124, 2006 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2006
  • Journal Name: ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.117-124
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Determining faults is a challenging task in complex systems. A discrete event system (DES) or a fuzzy discrete event system (FDES) approach with a fuzzy rule-base may resolve the ambiguity in a fault diagnosis problem especially in the case of multiple faults. In this study, an FDES approach with a fuzzy rule-base is used as a means of indicating the degree and priority of faults, especially in the case of multiple faults. The fuzzy rule-base is constructed using event-fault relations. Fuzzy events occurring any time with different membership degrees are obtained using k-means clustering algorithm. The fuzzy sub-event sequences are used to construct super events. The study is concluded by giving some examples about the distinguishability of fault types (parameter, actuator) in an unmanned small helicopter.