Identification and Robust Fault Detection of Industrial Gas Turbine Prototype Using LLNF Model
Publish place: Journal of Computer and Robotics، Vol: 5، Issue: 1
Publish Year: 1391
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JCR-5-1_005
تاریخ نمایه سازی: 23 دی 1396
Abstract:
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.
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Authors
Leila Shahmohamadi
Department of Electrical Engineering, Islamic Azad University, South Tehran Branch Tehran, Iran
Mahdi AliyariShoorehdeli
Faculty of Electrical Engineering, K. N. Toosi University of Technology Tehran, Iran
Sharareh Talaie
Department of Electrical Engineering, Islamic Azad University, South Tehran Branch Tehran, Iran