Linear-Matrix-Inequality-Based Fault Diagnosis for Gas Turbofan Engine Using Eigenstructure Assignment Principle

Yue Liu; Dao Liang Tan; Bin Wang; Xi Wang

doi:10.4028/www.scientific.net/AMM.302.759

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 302Linear-Matrix-Inequality-Based Fault Diagnosis for...

Linear-Matrix-Inequality-Based Fault Diagnosis for Gas Turbofan Engine Using Eigenstructure Assignment Principle

Abstract:

This paper proposes an eigenstructure assignment method for engine control system diagnosis based on disturbance decoupling, since noisy disturbance has an adverse impact on the performance of aircraft engine fault detection and isolation (FDI). In practice, it is often difficult to solve the eigenstructure assignment method, and the result is far from being satisfactory. In view of this, the paper makes an attempt to deal with the issue by linear matrix inequality (LMI). The advantages of the presented method are as follows: first, it can reduce the effect of exogenous disturbance on fault detection; In the meantime, it will not impair sensitivity to system faults. Experimental results show that the suggested approach performs well on the simulation of an advanced turbofan engine.

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Periodical:

Applied Mechanics and Materials (Volume 302)

Pages:

759-764

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.302.759

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Online since:

February 2013

Authors:

Yue Liu, Dao Liang Tan, Bin Wang, Xi Wang

Keywords:

Diagnosis, Eigenstructure Assignment, FDI, LMI, Robust Control, Turbofan

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References

[1] Wu, J.C.J., Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines. 2006, NDT & E International. p.304–311.

DOI: 10.1016/j.ndteint.2005.09.002

Google Scholar

[2] Ogaji, S.S.R., Advanced engine diagnostics using artificial neural networks. 2003, Applied Soft Computing. p.259–271.

DOI: 10.1016/s1568-4946(03)00038-3

Google Scholar

[3] Chen, J.P.R.J., Robust model-based fault diagnosis for dynamic systems. 1999, Kluwer Academic Publishers: Norwell, MA, USA.

Google Scholar

[4] Douglas, R.K., Robust Fault Detection Filter Design. Ph.D. Dissertation. 1993, Department of Aerospace Engineering and Engineering Mechanics, Univ. of Texas: Austin, TX.

Google Scholar

[5] Douglas, R.K.J.S., Robust Fault Detection Filter Design. 1996, Journal of Guidance, Control, and Dynamics. p.214–218.

Google Scholar

[6] Li, Z.J.I.M., Observer-based fault detection and isolation filter design for linear time-invariant systems. 2009, International Journal of Control. p.171–182.

DOI: 10.1080/00207170802031528

Google Scholar