DocumentCode
1749246
Title
A general design technique for fault diagnostic systems
Author
He, Jia-Zhou ; Zhou, Zhi-Hua ; Zhao, Zhi-Hong ; Chen, Shi-Fu
Author_Institution
Nat. Lab. for Novel Software Technol., Nanjing Univ., China
Volume
2
fYear
2001
fDate
2001
Firstpage
1307
Abstract
We put forward a design method for fault diagnostic systems (FDSs) by proposing a fault model and using the incremental hybrid learning algorithm which tightly combines symbolic learning and neural networks. It is capable of overcoming several shortcomings in existing diagnostic systems, such as the lack of universality, the unbalance in the use of fault prior knowledge and the dynamic data and the dilemma of stability and plasticity. Experiment showed the FDS implemented by this kind of method had a good diagnostic ability
Keywords
fault diagnosis; learning (artificial intelligence); neural nets; fault diagnostic systems; fault model; general design technique; incremental hybrid learning algorithm; neural networks; symbolic learning; Artificial intelligence; Design methodology; Fault diagnosis; Fault trees; Helium; Laboratories; Neural networks; Power system reliability; Stability; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939550
Filename
939550
Link To Document