Data-driven model reduction and fault diagnosis for an aero gas turbine engine

Lu, Yunjia and Gao, Zhiwei (2014) Data-driven model reduction and fault diagnosis for an aero gas turbine engine. In: IEEE 9th Conference on Industrial Electronics and Applications (ICIEA), 9-11 June, 2014, Hangzhou.

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Official URL: http://doi.org/10.1109/ICIEA.2014.6931485

Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Aero gas turbine engine; data-driven modeling; fault detection filter; genetic optimization algorithm
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Physics and Electrical Engineering
Depositing User: Zhiwei Gao
Date Deposited: 15 Jul 2014 09:07
Last Modified: 28 Jan 2016 15:42
URI: http://nrl.northumbria.ac.uk/id/eprint/17194

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