Title :
Fault detection and isolation in nonlinear dynamic systems: a fuzzy-neural approach
Author :
Chafi, Mehdi Sotudeh ; Akbarzadeb-T, M.-R. ; Moavenian, Majid
Author_Institution :
Dept. of Mech. Eng., Ferdowsi Univ., Mashhad, Iran
fDate :
6/23/1905 12:00:00 AM
Abstract :
A novel approach based on soft computing concepts is proposed for fault detection and isolation (FDI) of dynamic systems. The proposed method utilizes the concepts of fuzzy clustering, fuzzy decision making and RBF neural networks to create a suitable structure for design of a FDI. The practical applicability is illustrated on a CNC X-axis drive system. Specifically, FDI of twelve different process faults and three different sensor faults is successfully detected for the CNC system
Keywords :
computerised numerical control; decision theory; diagnostic expert systems; fault location; fuzzy neural nets; machining; nonlinear dynamical systems; pattern clustering; radial basis function networks; CNC X-axis drive system; FDI; RBF neural networks; fault detection; fault isolation; fuzzy clustering; fuzzy decision making; fuzzy-neural approach; nonlinear dynamic systems; soft computing concepts; Computer numerical control; Decision making; Electrical fault detection; Fault detection; Fault diagnosis; Feeds; Fuzzy neural networks; Mechanical engineering; Neural networks; Vehicle dynamics;
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-7293-X
DOI :
10.1109/FUZZ.2001.1008839
