Robust Fault Detection of Nonlinear Systems using Local Linear Neuro-Fuzzy Techniques with Application to a Gas turbine Engine

This study proposed a model-based robust fault detection (RFD) method using soft computing techniques. Robust detection of the possible incipient faults of an industrial gas turbine engine in steadystate conditions is mainly centered. For residual generation a bank of Multi-Layer perceptron (MLP) models, is used, Moreover, in fault detection phase, a passive approach based on Modellling Error Model (MEM) is employed to achieve robustness and threshold adaptation, and toward this purpose, Local Linear Neuro-Fuzzy (LLNF) model is exploited to construct error model to generate uncertainty interval upon the system output in order to make decision whether or not a fault occurred. This model is trained using the Locally Linear Model Tree (LOLIMOT) algorithm which is an incremental tree-structure algorithm, In order to show the effectiveness of proposed RFD method, it was tested on a single-shaft industrial gas turbine prototype model and has been evaluated using non-linear simulations, based on the gas turbine data.