Robust fault detection using iterative learning observer for nonlinear systems

2004 ◽  
Author(s):  
Ma Liling ◽  
Wang Junzheng ◽  
Wang Shoukun
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Iterative Learning ◽  
Robust Fault Detection

scholarly journals Neurofuzzy Approach to Fault Detection of Nonlinear Systems

1999 ◽  
Vol 3 (6) ◽  
pp. 524-531
Author(s):  
Jinglu Hu ◽  
◽  
Kotaro Hirasawa ◽  
Kousuke Kumamaru ◽  
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Linear Systems ◽  
Linear Model ◽  
Basis Function ◽  
Linear Models ◽  
Detection Method ◽  
Local Model ◽  
Model Based ◽  
Robust Fault Detection

This paper proposes a neurofuzzy approach to fault detection in linear systems. The system diagnosed is described by using a neurofuzzy model called LimNet that consists of a linear model and multiple local linear models with interpolation of a "fuzzy basis function". Fault detection is considered in two cases: when faults occur in the linear model part, a KDI-based robust fault detection is applied, where a multi-local-model part is treated as error due to nonlinear undermodeling; when faults occur in the multi-local-model part, a multi-model based fault detection method is developed, in which the identified LimNet is interpreted as several local ARMAX models, and KDI is used as an index to discriminate between each local model and its reference. This paper mainly concentrates discussions on multi-model based fault detection.

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