scholarly journals A New Approach to Robust Fault Detection of Model-unknown Nonlinear Systems

2014 ◽  
Vol 7 (8) ◽  
pp. 181-192
Author(s):  
Shengchao Su ◽  
Wei zhang
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
New Approach ◽  
Robust Fault Detection

Robust Fault Detection Based on Nonlinear Analytic Redundancy Techniques With Applications to Robotics

2005 ◽  
Author(s):  
Bibhrajit Halder ◽  
Nilanjan Sarkar
Keyword(s):  
Fault Detection ◽  
Design Method ◽  
Actuator Fault ◽  
Actuator Faults ◽  
Sensor Fault ◽  
New Approach ◽  
Model Plant ◽  
Robust Fault Detection ◽  
Highly Sensitive ◽  
Process Disturbances

A new approach to sensor and actuator fault detection in the presence of model uncertainty and disturbances, and its application to a wheeled mobile robot (WMR) are presented in this paper. Robust fault detection is important because of the universal existence of model uncertainties and process disturbances in most systems. This paper proposes a new approach, called robust nonlinear analytic redundancy (RNLAR) technique, to sensor and actuator fault detection for input-affine nonlinear multivariable dynamic systems in the presence of model-plant-mismatch and process disturbance. The proposed RNLAR can be used to design primary residual vectors (PRV) for nonlinear systems to detect sensor fault that are completely insensitive to both the model-plant-mismatch and process disturbance. It is shown that the PRV for actuator fault cannot be made completely insensitive to these factors. In order to overcome this problem, a nonlinear PRV design method to detect actuator faults is proposed where the PRVs are highly sensitive to the actuator faults and less sensitive to model-plant-mismatch and process disturbance. The proposed robust fault detection methodology is applied to a WMR and the simulation results are presented to demonstrate the effectiveness of this new approach.

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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