کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7104331 | 1460339 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
تکنولوژی و شیمی فرآیندی
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چکیده انگلیسی
In this paper, sensor fault detection and isolation of time-varying nonlinear dynamical systems is studied by utilizing an adaptive kernel principal component analysis (KPCA) solution as a useful method to overcome the weaknesses of conventional KPCA approach in dealing with time-varying dynamical processes. Toward this goal, adaptive Hotelling's T2 is used with KPCA to tackle the time-varying behavior of nonlinear systems. Moreover, for fault isolation, partial adaptive KPCA (AKPCA) is proposed where a set of residual signals is generated based on the structured residual set framework. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in the nonlinear dynamic model of an aeroderivative gas turbine can be effectively detected and isolated in the presence of component degradation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Process Control - Volume 64, April 2018, Pages 37-48
Journal: Journal of Process Control - Volume 64, April 2018, Pages 37-48
نویسندگان
Mania Navi, Nader Meskin, Mohammadreza Davoodi,