عرض بسيط للتسجيلة

المؤلفNavi, M.
المؤلفDavoodi, M.R.
المؤلفMeskin, Nader
تاريخ الإتاحة2022-04-14T08:45:42Z
تاريخ النشر2015
اسم المنشورIFAC-PapersOnLine
المصدرScopus
المعرّفhttp://dx.doi.org/10.1016/j.ifacol.2015.09.719
معرّف المصادر الموحدhttp://hdl.handle.net/10576/29800
الملخصIn this paper, partial kernel principal component analysis (PKPCA) is studied for sensor fault detection and isolation of an aeroderivative industrial gas turbine. Principal component analysis (PCA) is an effective tool for process monitoring task, however it can achieve acceptable results only for linear processes. In the case of nonlinear processes such as gas turbines, kernel PCA approach can be used which leads to more accurate health monitoring. In order to achieve fault isolation, partial KPCA is proposed where the parity relation concept is used to generate a set of residual signals. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in an industrial gas turbine can be effectively detected and isolated. 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
راعي المشروعQatar National Research Fund
اللغةen
الناشر9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015
الموضوعData handling
Gas turbines
Gases
Plant management
Process monitoring
Fault detection
Turbine components
Fault detection and isolation
Health monitoring
Industrial gas turbines
Kernel principal component analyses (KPCA)
Nonlinear process
Residual signals
Sensor fault detection
Simulation studies
Fault detection
Principal component analysis
العنوانSensor fault detection and isolation of an industrial gas turbine using partial kernel PCA
النوعConference Paper
الصفحات1389-1396
رقم العدد21
رقم المجلد28
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة