Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions

Abstract

Advanced diagnostic algorithms and high-fidelity simulation models improve the accuracy of model-based gas path fault diagnosis for gas turbines (GTs). But simultaneously, it becomes difficult in real-time applications due to the increased calculation amount. To improve the diagnosis speed, this study adopts the surrogate method to realize the real-time gas path fault diagnosis of GTs under transient operating conditions. First, the component level model (CLM) is built and verified. Subsequently, the surrogate model is established by combining the artificial neural network (ANN) and the necessary physical model. The constructed surrogate model can almost entirely reproduce the simulation results of CLM under the whole operation conditions. Finally, the real-time fault diagnosis system combines the surrogate model and the unscented Kalman filter (UKF). The results show that the surrogate model-based fault diagnosis system has the same accuracy as the CLM-based system. At the same time, the calculation speed is increased by nearly 50 times, which meets the real-time requirements of fault diagnosis.