Abstract
In this research, a hybrid system for fault detection and isolation (FDI) is proposed. The system incorporates the well-known benefits of residual generation approaches with a classification system for the automatic determination of thresholding. The architecture of the FDI system is divided in three levels: (i) the first one models the monitoring system signals (sensors) with a set of neural networks; (ii) the second makes the residual generation based on the difference of the actual output and the estimation of the neural network; ; (iii) and the third level assesses the set of residues using a classification system, at this stage the detection and fault isolation is done. The FDI system is evaluated in a simulated model of wind turbine with nine different types of issues. The results suggest a decrease in the number of false alarms, compared to a data-driven approach without loss in detection speed.
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