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Sensor fault detection and isolation of an industrial gas turbine using partial block-wise adaptive kernel peA

المؤلفNavi, Mania
المؤلفDavoodi, Mohammadreza
المؤلفMeskin, Nader
تاريخ الإتاحة2020-09-10T10:45:19Z
تاريخ النشر2017
اسم المنشور2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/CoDIT.2017.8102738
معرّف المصادر الموحدhttp://hdl.handle.net/10576/16040
الملخصIn this paper, sensor fault detection and isolation of nonlinear time-varying dynamical systems is investigated based on a fast partial block-wise adaptive Kernel Principal Component Analysis (KPCA) scheme. Using the proposed partial adaptive KPCA, faults are diagnosed perfectly and it is possible to prevail the shortcomings of the conventional KPCA and PCA methods. It is shown through simulation studies that the occurrence of sensor faults in the nonlinear dynamical model of an aeroderivative gas turbine can be detected and isolated effectively using the proposed approach. 1 2017 IEEE.
راعي المشروعThis publication was made possible by NPRP grant No.5 - 574 - 2 -233 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعAdaptive kernel PCA
Aeroderivative gas turbine
Dynamical time-varying systems
Fault detection
Isolation
العنوانSensor fault detection and isolation of an industrial gas turbine using partial block-wise adaptive kernel peA
النوعConference Paper
الصفحات1054-1059
رقم المجلد2017-January


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