Title :
Component fault diagnosis using dynamic co-active neuro-fuzzy systems
Author :
Mirea, Letitia ; Patton, Ron J.
Author_Institution :
Dept. of Autom. Control & Ind. Inf., Gh. Asachi Tech. Univ. of Iasi, Iasi, Romania
Abstract :
This paper investigates the development of a new type of dynamic neuro-fuzzy system with neuronal rules and its application to fault detection and isolation (FDI) of components of a dynamic process. Hybrid learning based on fuzzy clustering algorithm and the steepest-descent method, is used to train the proposed neuro-fuzzy system. The experimental case study concerns the component fault diagnosis of a three-tank system. A neuro-fuzzy simplified observer scheme is used to generate the residuals (symptoms) in the form of one step-ahead prediction errors. These are then analysed by a neural classifier in order to take the appropriate decision regarding the actual process behaviour.
Keywords :
fault diagnosis; fuzzy neural nets; fuzzy set theory; fuzzy systems; gradient methods; learning (artificial intelligence); neurocontrollers; observers; pattern clustering; tanks (containers); volume control; FDI; component fault diagnosis; dynamic coactive neuro-fuzzy systems; fault detection-and-isolation; fuzzy clustering algorithm; hybrid learning; neural classifier; neuro-fuzzy simplified observer scheme; neuronal rules; residual generation; steepest-descent method; step-ahead prediction errors; three-tank system; Biological neural networks; Clustering algorithms; Fault diagnosis; Observers; Training; Vectors;
Conference_Titel :
Control Conference (ECC), 2007 European
Conference_Location :
Kos
Print_ISBN :
978-3-9524173-8-6
