Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; 2001
DOI: 10.1115/2001-gt-0019
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Global Nonlinear Modelling of Gas Turbine Dynamics Using NARMAX Structures

Abstract: This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modelling and nonlinear analysis is used to restrict the search space for nonlinear modelling. The nonlinear model is then validated using large-si… Show more

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Cited by 3 publications
(3 citation statements)
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“…In order to identify a model capable of representing the engine at all operating points, Rodriguez [9] used a multi objective genetic programming approach on the same data and allocated weights to various objectives, to assess their significance in the structure selection of Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX) models of the engine. Chiras et al [10,11] used nonparametric data analysis in both time-and frequency-domains and an orthogonal estimation algorithm to estimate NARMAX models of the engine. A simple NARX model was identified which was able to represent both the small and large signal dynamics of the engine.…”
Section: Introductionmentioning
confidence: 99%
“…In order to identify a model capable of representing the engine at all operating points, Rodriguez [9] used a multi objective genetic programming approach on the same data and allocated weights to various objectives, to assess their significance in the structure selection of Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX) models of the engine. Chiras et al [10,11] used nonparametric data analysis in both time-and frequency-domains and an orthogonal estimation algorithm to estimate NARMAX models of the engine. A simple NARX model was identified which was able to represent both the small and large signal dynamics of the engine.…”
Section: Introductionmentioning
confidence: 99%
“…In the area of aero gas turbine models, one can refer to the efforts of Chiras et al [3]- [6], Ruano et al [7], and Torella et al [8]. They employed a variety of ANN-based techniques and approaches such as MLP, nonlinear auto-regressive moving average with exogeneous inputs (NARMAX), nonlinear autoregressive exogenous model (NARX), radial basis function (RBS), back propagation neural networks (BPNN) and Bspline, to explore nonlinear dynamics of aero gas turbines.…”
mentioning
confidence: 99%
“…For instance, Chiras et al [3]- [6], Ruano et al [7], and Torella et al [8], concentrated on aero gas turbines. Jurado [10], and Bartolini et al [11], investigated micro gas turbines.…”
mentioning
confidence: 99%