Sensor fault diagnosis of a power plant: an approach based on state estimation techniques

This paper addresses a model-based procedure exploiting analytical redundancy for the detection and isolation of faults of a power plant. The residual generation is performed by means of output observers and Kalman filters in connection with the uncertainty affecting the measurements acquired from the monitored system. The model of the process under investigation required to design observers and filters is obtained by identification. The proposed fault detection and isolation tool has been tested on a simulated model of an industrial gas turbine prototype.