identification and robust fault detection of industrial gas turbine prototype using llnf model

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.