Structural optimization of the neural network model for the gas turbine engine monitoring

Novikova, Svetlana

doi:10.3103/s1068799816020185

Cited by 11 publications

(1 citation statement)

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“…However, several works have been carried out in this field to overcome the gas turbines modeling issues and their use in control and diagnosis [1][2][3][4][5][6][7][8][9][10][11]. In 2020, Evans et al [12] carried out an identification of the dynamics of gas turbines using signal analysis techniques in the frequency domain for the purpose of turbine control.…”

Section: Introductionmentioning

confidence: 99%

Identification of Two-shaft Gas Turbine Variables Using a Decoupled Multi-model Approach With Genetic Algorithm

Aissat

Hafaifa

Iratni

et al. 2021

Period. Polytech. Mech. Eng.

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In industrial practice, the representation of the dynamics of nonlinear systems by models linking their different operating variables requires an identification procedure to characterize their behavior from experimental data. This article proposes the identification of the variables of a two-shafts gas turbine based on a decoupled multi-model approach with genetic algorithm. Hence the multi-model is determined in the form of a weighted combination of the decoupled linear local state space sub-models, with optimization of an objective cost function in different modes of operation of this machine. This makes it possible to have robust and reliable models using input / output data collected on the examined system, limiting the influence of errors and identification noises.

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Section: Introductionmentioning

confidence: 99%

Identification of Two-shaft Gas Turbine Variables Using a Decoupled Multi-model Approach With Genetic Algorithm

Aissat

Hafaifa

Iratni

et al. 2021

Period. Polytech. Mech. Eng.

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The Neural Modeling for the Assessment of Hazardous Hydro Meteorological Phenomena Including the Case of the Providing a Sustainable Work (Reliability) on Transport

Haritonova

2017

Advances in Intelligent Systems and Computing

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Understanding the Effect of Hyperparameter Optimization on Machine Learning Models for Structure Design Problems

Zhu

2021

Computer-Aided Design

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Structural optimization of the neural network model for the gas turbine engine monitoring

Cited by 11 publications

References 1 publication

Identification of Two-shaft Gas Turbine Variables Using a Decoupled Multi-model Approach With Genetic Algorithm

Identification of Two-shaft Gas Turbine Variables Using a Decoupled Multi-model Approach With Genetic Algorithm

The Neural Modeling for the Assessment of Hazardous Hydro Meteorological Phenomena Including the Case of the Providing a Sustainable Work (Reliability) on Transport

Understanding the Effect of Hyperparameter Optimization on Machine Learning Models for Structure Design Problems

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