Ensemble-Based Fault Detection and Isolation of an Industrial Gas Turbine
Document Type
Conference Proceeding
Publication Date
10-11-2020
Publication Title
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume
2020-October
First Page
2351
Keywords
decision tree, ensemble-based learning, fault detection and isolation (FDI), gas turbine
Last Page
2358
Abstract
In this study, an efficient strategy for fault detection and isolation (FDI) of an Industrial Gas Turbine is introduced based on ensemble learning methods. Four independent Wiener models are identified by employing plant input/output data to determine system behavior. Following that, an ensemble-based method is established, which utilizes all the Wiener models and relevant residuals to detect the faults. A fault isolation structure is then developed based on ensemble bagged tree procedure such that it is capable of isolating faults in a steady-state runtime. As a crucial goal, increasing accuracy and robustness simultaneously are mainly centered. The proposed FDI method is tested on nonlinear gas turbine simulation using real data from a combined cycle power plant. The obtained results illustrate the correctness and accuracy of the presented FDI scheme.
DOI
10.1109/SMC42975.2020.9282904
ISSN
1062922X
ISBN
9781728185262
Recommended Citation
Mousavi, Mehdi; Moradi, Milad; Chaibakhsh, Ali; Kordestani, Mojtaba; and Saif, Mehrdad. (2020). Ensemble-Based Fault Detection and Isolation of an Industrial Gas Turbine. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2020-October, 2351-2358.
https://scholar.uwindsor.ca/electricalengpub/242