Ensemble-Based Fault Detection and Isolation of an Industrial Gas Turbine

In this study, an efficient strategy for fault detection and isolation (FDI) of an Industrial Gas Turbine is introduced based on ensemble learning methods. Four independent Wiener models are identified by employing plant input/output data to determine system behavior. Following that, an ensemble-based method is established, which utilizes all the Wiener models and relevant residuals to detect the faults. A fault isolation structure is then developed based on ensemble bagged tree procedure such that it is capable of isolating faults in a steady-state runtime. As a crucial goal, increasing accuracy and robustness simultaneously are mainly centered. The proposed FDI method is tested on nonlinear gas turbine simulation using real data from a combined cycle power plant. The obtained results illustrate the correctness and accuracy of the presented FDI scheme.