Research for Fault Diagnosis of Aeroengine Based on Fuzzy Neural Network

Tian, Feng; Yin, Dehao; Zhang, Rui; Wu, Yanyan

doi:10.1109/imccc.2013.144

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2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control 2013

DOI: 10.1109/imccc.2013.144

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Research for Fault Diagnosis of Aeroengine Based on Fuzzy Neural Network

Feng Tian¹,

Dehao Yin²,

Rui Zhang³

et al.

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2019

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Robust Fault Identification of Turbofan Engines Sensors Based on Fractional-Order Integral Sliding Mode Observer

Xiao

2019

International Journal of Turbo &Amp; Jet-Engines

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This paper presents a robust fault identification scheme based on fractional-order integral sliding mode observer (FOISMO) for turbofan engine sensors with uncertainties. The equilibrium manifold expansion (EME) model is introduced due to its simplicity and accuracy for nonlinear system. A fractional-order integral sliding mode observer is designed to reconstruct faults on sensors, in which the fractional-order integral sliding surface guarantees the fast convergence of reconstruction. The observer parameters is selected according to L2 gain theory in order to minimize the effect of uncertainties on the fault reconstruction signal. Simulations in Matlab/Simulink show high reconstruction accuracy of the proposed method despite the present of uncertainties.

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Robust Fault Identification of Turbofan Engines Sensors Based on Fractional-Order Integral Sliding Mode Observer

Xiao

2019

International Journal of Turbo &Amp; Jet-Engines

View full text Add to dashboard Cite

show abstract

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Research for Fault Diagnosis of Aeroengine Based on Fuzzy Neural Network

Cited by 1 publication

References 3 publications

Robust Fault Identification of Turbofan Engines Sensors Based on Fractional-Order Integral Sliding Mode Observer

Robust Fault Identification of Turbofan Engines Sensors Based on Fractional-Order Integral Sliding Mode Observer

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