Fault diagnosis of machines via parameter estimation and knowledge processing: tutorial paper
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Neuro-fuzzy architectures and hybrid learning
Neuro-fuzzy architectures and hybrid learning
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)
Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Expert Systems with Applications: An International Journal
Neuro-fuzzy networks and their application to fault detection of dynamical systems
Engineering Applications of Artificial Intelligence
Online fault detection and isolation of nonlinear systems based on neurofuzzy networks
Engineering Applications of Artificial Intelligence
Genetically programmed-based artificial features extraction applied to fault detection
Engineering Applications of Artificial Intelligence
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This paper presents a neuro-fuzzy (NF) networks based scheme for fault detection and isolation (FDI) of a U-tube steam generator (UTSG) in a nuclear power plant. Two types of NF networks are used. A NF based learning and adaptation of Takagi-Sugeno (TS) fuzzy models is used for residual generation, while for residual evaluation a NF network for Mamdani models is used. The NF network for Takagi-Sugeno models is trained with data collected from a full scale UTSG simulator and is used for generating residuals in the fault detection step. A locally linear neuro-fuzzy (LLNF) model is used in the identification of the steam generator. This model is trained using the locally linear model tree (LOLIMOT) algorithm. In the fault isolation part, genetic algorithms are employed to train a Mamdani type NF network, which is used to classify the residuals and take the appropriate decision regarding the actual behavior of the process. Furthermore, a qualitative description of faults is then extracted from the fuzzy rules obtained from the Mamdani NF network. Experimental results presented in the final part of the paper confirm the effectiveness of this approach.