Application of data driven methods in diagnostic of selected process faults of nuclear power plant steam turbine

Abstract

Article presents a comparison of process anomaly detection in nuclear power plant steam turbine using combination of data driven methods. Three types of faults are considered: water hammering, fouling and thermocouple fault. As a virtual plant a nonlinear, dynamic, mathematical steam turbine model is used. Two approaches for fault detection using one class and two class classifiers are tested and compared.