Data-Driven Model Reduction And Fault Diagnosis For An Aero Gas Turbine Engine

被引：0
|
作者：
Lu, Yunjia [1 ]
Gao, Zhiwei [1 ]
机构：
[1] Northumbria Univ Newcastle, Fac Engn & Environm, Newcastle Upon Tyne, Tyne & Wear, England
来源：
PROCEEDINGS OF THE 2014 9TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA) | 2014年
关键词：
Aero gas turbine engine; data-driven modeling; fault detection filter; genetic optimization algorithm;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, an aero gas turbine engine with three shafts are investigated. By employing data-driven method, a reduced-order model is obtained, which has the close output performance as the 14th-order full-order model. Based on the reduced-order model, a fault detection filter is designed to detect actuator faults and sensor faults for the system subjected to input and output noises. Genetic optimization algorithm is used to design the filter gains such that the residual signal is sensitive to the faults, but robust to process and sensor noises. Simulated results demonstrate the efficiency of the present algorithm.
引用
收藏
页码：1936 / 1941
页数：6
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