DocumentCode
2316262
Title
Using neural networks for fault diagnosis
Author
He, Jia-Zhou ; Zhou, Zhi-Hua ; Yin, Xu-Ri ; Chen, Shi-Fu
Author_Institution
State Key Lab. for Novel Software Technol., Nanjing Univ., China
Volume
5
fYear
2000
fDate
2000
Firstpage
217
Abstract
A universal fault instance model, which aims to solve problems existing in the present technology of fault diagnosis, such as the lack of universality, the difficulty in the use of real time systems and the dilemma of stability and plasticity, is proposed. An experiment demonstrates that the FANNC used can successfully settle the problems mentioned above by its effective incremental ability and processing new input patterns via one round learning
Keywords
fault diagnosis; neural nets; pattern classification; FANNC; fast adaptive neural network classifier; incremental ability; one round learning; plasticity; real time systems; stability; universal fault instance model; universality; Artificial neural networks; Automatic control; Fault detection; Fault diagnosis; Helium; Laboratories; Neural networks; Pattern analysis; Real time systems; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.861460
Filename
861460
Link To Document