Study of hybrid intelligent fault diagnosis

Author

Lou, Guohuan ; Zhou, Yuan ; Yao, Zheng

Author_Institution

Coll. of Comput. & Autom. Control, Hebei Polytech. Univ., Tangshan, China

Volume

1

fYear

2010

fDate

6-7 March 2010

Firstpage

174

Lastpage

177

Abstract

A hybrid intelligent fault diagnosis method is presented for the diversity, uncertainty and complexity of device faults. This method integrates respective advantages of fault tree, fuzzy theory, neural networks and genetic algorithms to form a hybrid approach and is applied to fault diagnosis of fan. Experiments show that this method is simple and effective. It can also be applied to other fault diagnosis of complex systems and has certain portability.

Keywords

fans; fault diagnosis; fault trees; fuzzy set theory; genetic algorithms; large-scale systems; mechanical engineering computing; neural nets; complex systems; device complexity; device diversity; fan; fault tree; fuzzy theory; genetic algorithms; hybrid intelligent fault diagnosis method; neural networks; Automatic control; Data mining; Educational institutions; Fault diagnosis; Fault trees; Fuzzy neural networks; Genetic algorithms; Intelligent robots; Neural networks; Robotics and automation; fault diagnosis; fuzzy fault tree; genetic neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on

Conference_Location

Wuhan

ISSN

1948-3414

Print_ISBN

978-1-4244-5192-0

Electronic_ISBN

1948-3414

Type

conf

DOI

10.1109/CAR.2010.5456876

Filename

5456876