Sensor fault detection and isolation of an industrial gas turbine using partial block-wise adaptive kernel peA
| Author | Navi, Mania |
| Author | Davoodi, Mohammadreza |
| Author | Meskin, Nader |
| Available date | 2020-09-10T10:45:19Z |
| Publication Date | 2017 |
| Publication Name | 2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017 |
| Resource | Scopus |
| 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. 1 2017 IEEE. |
| Sponsor | This 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. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | Adaptive kernel PCA Aeroderivative gas turbine Dynamical time-varying systems Fault detection Isolation |
| Type | Conference Paper |
| Pagination | 1054-1059 |
| Volume Number | 2017-January |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
|
There are no files associated with this item. |
|||
This item appears in the following Collection(s)
-
Electrical Engineering [2429 items ]