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

Yunjia Lu; Zhiwei Gao

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

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

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.

Original language	English
Publication status	Published - 9 Jun 2014
Event	IEEE 9th Conference on Industrial Electronics and Applications (ICIEA) - Hangzhou Duration: 9 Jun 2014 → … http://www.ieeeiciea.org/2014/

Conference

Conference	IEEE 9th Conference on Industrial Electronics and Applications (ICIEA)
Period	9/06/14 → …
Internet address	http://www.ieeeiciea.org/2014/

Keywords

Aero gas turbine engine
data-driven modeling
fault detection filter
genetic optimization algorithm

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

Cite this

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title = "Data-driven model reduction and fault diagnosis for an aero gas turbine engine",

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.",

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AU - Gao, Zhiwei

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N2 - 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.

AB - 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.

KW - Aero gas turbine engine

KW - data-driven modeling

KW - fault detection filter

KW - genetic optimization algorithm

UR - https://www.scopus.com/pages/publications/84912145039

M3 - Paper

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ER -

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

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Keywords

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