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

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.