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
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861460
