scholarly journals Neural Networks for Gas Turbine Diagnosis

10.5772/63107 ◽  
2016 ◽  
Author(s):  
Igor Loboda
Keyword(s):  
Neural Networks ◽  
Gas Turbine

Using Neural Networks in Controlling Low- and Medium-Capacity Gas-Turbine Plants

2019 ◽  
Vol 90 (11) ◽  
pp. 737-740 ◽  
Author(s):  
B. V. Kavalerov ◽  
I. V. Bakhirev ◽  
G. A. Kilin
Keyword(s):  
Neural Networks ◽  
Gas Turbine

Combination of Neural Networks and State Vector Feedback Adaptive Control (SVFAC) Technique to Control the Gas Turbine Combustor

2006 ◽  
Author(s):  
Mahmood Lahroodi ◽  
A. A. Mozafari
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Control System ◽  
Gas Turbine ◽  
Control Method ◽  
Control Performance ◽  
Gas Turbine Combustor ◽  
Model Network ◽  
Neural Network Controller ◽  
Nonlinear Plants

Neural networks have been applied very successfully in the identification and control of dynamic systems. When designing a control system to ensure the safe and automatic operation of the gas turbine combustor, it is necessary to be able to predict temperature and pressure levels and outlet flow rate throughout the gas turbine combustor to use them for selection of control parameters. This paper describes a nonlinear SVFAC controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the affection of nonlinear factors contained in controller. The neural network controller learns to produce the input selected by the optimization process. The controller is adaptively trained to force the plant output to track a reference output. Proposed Adaptive control system configuration uses two neural networks: a controller network and a model network. The model network is used to predict the effect of controller changes on plant output, which allows the updating of controller parameters. This paper presents the new adaptive SFVC controller using neural networks with compensation for nonlinear plants. The control performance of designed controller is compared with inverse control method and results have shown that the proposed method has good performance for nonlinear plants such as gas turbine combustor.

An Ensemble of Recurrent Neural Networks for Real Time Performance Modelling of Three-Spool Aero-Derivative Gas Turbine Engine

2021 ◽  
Author(s):  
Ibrahem Mohamed Ibrahem ◽  
Ouassima Akhrif ◽  
Hany Moustapha ◽  
Martin Staniszewski
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Performance Modelling ◽  
Weighting Method ◽  
Turbine Engine ◽  
Single Output ◽  
Operation Conditions ◽  
Unseen Data ◽  
Narx Models

Abstract Gas turbine is a complex system operating in non-stationary operation conditions for which traditional model-based modeling approaches have poor generalization capabilities. To address this, an investigation of a novel data-driven neural networks based model approach for a three-spool aero-derivative gas turbine engine (ADGTE) for power generation during its loading and unloading conditions is reported in this paper. For this purpose, a non-linear autoregressive network with exogenous inputs (NARX) is used to develop this model in MATLAB environment using operational closed-loop data collected from Siemens (SGT-A65) ADGTE. Inspired by the way biological neural networks process information and by their structure which changes depending on their function, multiple-input single-output (MISO) NARX models with different configurations were used to represent each of the ADGTE output parameters with the same input parameters. Usage of a single neural network to represent each of the system output parameters may not be able to provide an accurate prediction for unseen data and as a consequence, provides poor generalization. To overcome this problem, an ensemble of MISO NARX models is used to represent each output parameter. The major challenge of the ensemble generation is to decide how to combine results produced by the ensemble's components. In this paper, a novel hybrid dynamic weighting method (HDWM) is proposed. The simulation results show improvement in accuracy and robustness by using the proposed modeling approach.

scholarly journals Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine

2004 ◽  
Vol 78 (4) ◽  
pp. 397-418 ◽  
Author(s):  
R.B. Joly ◽  
S.O.T. Ogaji ◽  
R. Singh ◽  
S.D. Probert
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Turbofan Engine ◽  
Artificial Neural ◽  
Bypass Ratio

scholarly journals Gas Turbine Exhaust System Health Management Based on Recurrent Neural Networks

Procedia CIRP ◽  
2019 ◽  
Vol 83 ◽  
pp. 630-635 ◽  
Author(s):  
Fei Zhao ◽  
Liang Chen ◽  
Tangbin Xia ◽  
Zikun Ye ◽  
Yu Zheng
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Recurrent Neural Networks ◽  
Health Management ◽  
Exhaust System ◽  
Gas Turbine Exhaust ◽  
Turbine Exhaust

Assessment of the Robustness of Gas Turbine Diagnostics Tools Based on Neural Networks

2006 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini ◽  
G. A. Zanetta
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Measurement Errors ◽  
Health State ◽  
Artificial Intelligence Technique ◽  
Boundary Load ◽  
Set Up ◽  
Operating Points ◽  
Range Of Variation ◽  
Selection Of

The paper deals with the set-up and the application of an Artificial Intelligence technique based on Neural Networks (NNs) to gas turbine diagnostics, in order to evaluate its capabilities and its robustness. The data used for both training and testing the NNs were generated by means of a Cycle Program, calibrated on a Siemens V94.3A gas turbine. Such data are representative of operating points characterized by different boundary, load and health state conditions. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, by evaluating NN robustness with respect to: • interpolation capability and accuracy in the presence of data affected by measurement errors; • extrapolation capability in the presence of data lying outside the range of variation adopted for NN training; • accuracy in the presence of input data corrupted by bias errors; • accuracy when one input is not available. This situation is simulated by replacing the value of the unavailable input with its nominal value.

Identification of Aircraft Gas-Turbine Dynamics using B-splines Neural Networks

2000 ◽  
Vol 33 (6) ◽  
pp. 123-128 ◽  
Author(s):  
J.C. Lopes ◽  
A.E. Ruano ◽  
P.J. Fleming
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
B Splines

Intelligent performance diagnostics of a gas turbine engine using user‐friendly interface neural networks

2004 ◽  
Vol 76 (4) ◽  
pp. 391-397 ◽  
Author(s):  
Chang‐duk Kong ◽  
Ja‐young Ki ◽  
Myoung‐cheol Kang ◽  
Seong‐hee Kho
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Turbine Engine ◽  
User Friendly ◽  
Performance Diagnostics
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