Title :
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
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;
Conference_Titel :
Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5192-0
Electronic_ISBN :
1948-3414
DOI :
10.1109/CAR.2010.5456876
