Neuro-fuzzy Takagi Sugeno observer for fault diagnosis in wind turbines

This work proposes a method for fault diagnosis based on Takagi Sugeno (TS) observers and convex models identified with a multioutput adaptive neuro-fuzzy inference system (MANFIS) derived from structural analysis. A bank of zonotopic TS observers is implemented to detect sensors and actuators faults. Unlike other works that require data from fault scenarios to train the MANFIS neural network, only fault-free data are considered. In addition, uncertainty related to aerodynamic loads and measurement noise is considered for testing the proposed method's robustness. The method performance is evaluated using measurements from a 5 MW wind turbine benchmark.