Title :
Dynamic system failure detection and diagnosis employing sliding mode observers and fuzzy neural networks
Author :
Caminhas, Walmir M. ; Takahashi, Ricardo H C
Author_Institution :
Dept. of Electr. Eng., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
Abstract :
A strategy for dynamic system failure detection and diagnosis is proposed, based on sliding mode observers, employed for residual generation with discrimination among the error subspaces, and a fuzzy neural network used for pattern classification. A control reconfiguration scheme is proposed, employing both the fault diagnosis information and the robust observer generated data. The resulting structure has been evaluated in a simulated D.C. electric drive
Keywords :
DC machines; digital simulation; fault diagnosis; fuzzy neural nets; neurocontrollers; observers; pattern classification; power system simulation; variable structure systems; control reconfiguration scheme; dynamic system failure detection; dynamic system failure diagnosis; error subspaces; fault diagnosis information; fuzzy neural network; pattern classification; residual generation; robust observer generated data; simulated DC electric drive; sliding mode observers; Fault detection; Fault diagnosis; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Integrated circuit modeling; Neural networks; Observers; Pattern classification; Sliding mode control;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.944269
