Sensor fault detection and isolation of an industrial gas turbine using partial block-wise adaptive kernel peA
Abstract
In 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.
Publication Title
2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017
Recommended Citation
Navi, M., Davoodi, M., & Meskin, N. (2017). Sensor fault detection and isolation of an industrial gas turbine using partial block-wise adaptive kernel peA. 2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017, 2017-January, 1054-1059. https://doi.org/10.1109/CoDIT.2017.8102738
