Fault diagnosis with dynamic fuzzy discrete event system approach

Authors:
Erdal Kılıç;Çağlar Karasu;Kemal Leblebicioğlu
Affiliations:
Electrical and Electronic Engineering Department Computer Vision and Intelligent Systems Laboratory, Middle East Technical University, Ankara, Turkey;Tübitak, Sage, Ankara, Turkey;Electrical and Electronic Engineering Department Computer Vision and Intelligent Systems Laboratory, Middle East Technical University, Ankara, Turkey
Venue:
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Year:
2005

Citing 9
Cited 1

A new technique for fault detection using Petri nets

Automatica (Journal of IFAC)
Neuro-fuzzy systems for diagnosis

Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Fuzzy Control

Fuzzy Control
A Framework of Fuzzy Diagnosis

IEEE Transactions on Knowledge and Data Engineering
Introduction to Discrete Event Systems

Introduction to Discrete Event Systems
Modeling and control of fuzzy discrete event systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On fuzzy logic applications for automatic control, supervision, and fault diagnosis

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Handling uncertainty with possibility theory and fuzzy sets in a satellite fault diagnosis application

IEEE Transactions on Fuzzy Systems
Fuzzy set-based methods in instance-based reasoning

IEEE Transactions on Fuzzy Systems

Mobile robot behavior coordination using supervisory control of fuzzy discrete event systems

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems

Quantified Score

Hi-index	0.00

Visualization

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.