سال انتشار: ۱۳۹۰

محل انتشار: سومین کنفرانس ملی صنعت نیروگاههای حرارتی

تعداد صفحات: ۶

نویسنده(ها):

Iman Yousefi – 1MAPNA Electrical and Control Engineering and Manufacturing Company – MECO
Hamid Khaloozadeh – K. N. Toosi University of Technology
Ali Ashraf Modarres –

چکیده:

The objective of this paper is to model, identify, and detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called residuals that areerrors between estimated and measured variables of the process. An ARX model is used for residual generation, while for residualevaluation a neural network classifier for MLP is used. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model.