Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors

2005 ◽  
Vol 19 (1) ◽  
pp. 55-66 ◽  
Author(s):  
Alexandre Evsukoff ◽  
Sylviane Gentil
Keyword(s):  
Fault Detection ◽  
Fuzzy System ◽  
Nuclear Reactors ◽  
Fault Detection And Isolation ◽  
Neuro Fuzzy

scholarly journals Neuro-Fuzzy for Sensor Fault Detection and Isolation

2014 ◽  
Vol 90 (17) ◽  
pp. 42-46
Author(s):  
Rajendra Sharma ◽  
Snehal Kokil ◽  
Prtit Khaire
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Neuro Fuzzy ◽  
Sensor Fault Detection

Fault Detection and Isolation for an Uncertain Takagi-Sugeno Fuzzy System using the Interval Approach

2015 ◽  
pp. 364-389 ◽  
Author(s):  
Hassene Bedoui ◽  
Atef Kedher ◽  
Kamel Ben Othman
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Fuzzy System ◽  
Fault Detection And Isolation ◽  
Upper And Lower Bounds ◽  
Multiple Model ◽  
Interval Approach ◽  
System Uncertainties ◽  
Takagi Sugeno ◽  
Detection And Localization

This work deals with the fault detection and localization in the case of uncertain nonlinear systems. The presented method uses the diagnosis based on mathematical models. To model nonlinear systems, the multiple model approach is used. This method uses the Takagi-Sugeno fuzzy systems principle to obtain a nonlinear system named multiple models. This modeling principle has the advantage of obtaining a general model that can describe any class of nonlinear systems. This modeling principle also allows one to obtain the generalization of many results that are already obtained for linear systems to the nonlinear systems. To model the system uncertainties, the interval approach is used because the faults or disturbances are generally unknown, but it is possible to know their upper and lower bounds. The proposed technique is insensitive to measurement uncertainties and highly reliable in case of a fault affecting the outputs system.

Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks

2009 ◽  
Vol 72 (13-15) ◽  
pp. 2939-2951 ◽  
Author(s):  
Roozbeh Razavi-Far ◽  
Hadi Davilu ◽  
Vasile Palade ◽  
Caro Lucas
Keyword(s):  
Fault Detection ◽  
Steam Generator ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Neuro Fuzzy

Fault Detection and Isolation for an Uncertain Takagi-Sugeno Fuzzy System using the Interval Approach

Fuzzy Systems ◽  
2017 ◽  
pp. 393-416
Author(s):  
Hassene Bedoui ◽  
Atef Kedher ◽  
Kamel Ben Othman
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Fuzzy System ◽  
Fault Detection And Isolation ◽  
Upper And Lower Bounds ◽  
Multiple Model ◽  
Interval Approach ◽  
System Uncertainties ◽  
Takagi Sugeno ◽  
Detection And Localization

This work deals with the fault detection and localization in the case of uncertain nonlinear systems. The presented method uses the diagnosis based on mathematical models. To model nonlinear systems, the multiple model approach is used. This method uses the Takagi-Sugeno fuzzy systems principle to obtain a nonlinear system named multiple models. This modeling principle has the advantage of obtaining a general model that can describe any class of nonlinear systems. This modeling principle also allows one to obtain the generalization of many results that are already obtained for linear systems to the nonlinear systems. To model the system uncertainties, the interval approach is used because the faults or disturbances are generally unknown, but it is possible to know their upper and lower bounds. The proposed technique is insensitive to measurement uncertainties and highly reliable in case of a fault affecting the outputs system.

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