سال انتشار: ۱۳۸۶
محل انتشار: اولین کنگره مشترک سیستم های فازی و سیستم های هوشمند
تعداد صفحات: ۸
Roozbeh Razavi Far – Department of Nuclear Engineering, Amirkabir University of Technology, Tehran, Iran
Hadi Davilu – Department of Nuclear Engineering, Amirkabir University of Technology, Tehran, Iran
Caro Lucas – Center of Excellence on Control and Intelligent Processing,Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
measurements coming from the plant and a nominal model. The neural network estimator is trained with healthy data collected from a full-scale simulator. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. These patterns are stored in an associative memory based on a recurrent neural network. The proposed fault diagnosis tool is evaluated on-line via a full-scale simulator to detect and isolate the main faults appearing in the pressurizer of a PWR.