Identification and Robust Fault Detection of Industrial Gas Turbine Prototype Using LLNF Model


1 Department of Electrical Engineering, Islamic Azad University, South Tehran Branch Tehran, Iran

2 Faculty of Electrical Engineering, K. N. Toosi University of Technology Tehran, Iran


In this study, detection and identification of common faults in industrial gas turbines is investigated. We propose a model-based robust fault detection(FD) method based on multiple models. For residual generation a bank of Local Linear Neuro-Fuzzy (LLNF) models is used. Moreover, in fault detection step, a passive approach based on adaptive threshold is employed. To achieve this purpose, the adaptive threshold band is made by a sliding window technique to make decision whether a fault occurred or not. In order to show the effectiveness of proposed FD method, it is used to identify a simulated single-shaft industrial gas turbine prototype model, which works in various operation points. This model is a reference simulation which is used in many similar researches with the aim of fault detection in gas turbines.