System identification strategies applied to aircraft gas turbine engines

Arkov, V.Y.; Evans, D.C.; Fleming, Peter; Hill, David; Norton, J.P.; Pratt, I. H.; Rees, D.; Rodríguez-Vázquez, Katya

doi:10.1016/s1474-6670(17)56745-2

Cited by 8 publications

(10 citation statements)

References 5 publications

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“…This is a commonly used value (Doherty et al, 1997, Arkov et al, 2000 and exceeds the maximum time delay encountered during both of the case studies used in this work. In practice, the results of the tests must be analysed carefully to ensure that max τ is not less than the maximum lag required for an accurate model.…”

Section: -10mentioning

confidence: 92%

“…The tests were designed to determine whether a proposed model captures the dynamics of the underlying process as opposed to simply fitting the available data. These tests have been applied to a number of non-linear dynamic systems including real and simulated processes (Doherty et al, 1997, Arkov et al, 2000. In addition, it has been shown how the test results can be used to identify process lags that are missing from the input sequence.…”

Section: Residual Analysismentioning

confidence: 99%

“…Later, Arkov et al (2000) extended the work by including the additional tests for non-linear systems. Although the authors describe how the technique can be used to develop accurate models, the results were not compared with algorithms that did not use multi-objective techniques.…”

Section: Residual Analysismentioning

confidence: 99%

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Dynamic systems modelling using genetic programming

Hinchliffe¹,

Willis²

2003

Computers & Chemical Engineering

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Genetic programming (GP) is an evolutionary algorithm that attempts to evolve solutions to a problem by using concepts taken from the naturally occurring evolutionary process. This thesis introduces the concepts of GP model development by applying the technique to steady-state model evolution. A variation of the algorithm known as the multiple basis function GP (MBF-GP) algorithm is described and its performance compared with the standard algorithm. Results show that the MBF-GP algorithm requires significantly less computational effort to evolve models of comparable accuracy to the standard algorithm. The steady-state algorithm is then modified to enable the evolution of dynamic process models. Three case studies are used to demonstrate algorithm performance and show how the MBF-GP algorithm produces performance benefits similar to those observed in the steady-state modelling work. A comparison with neural networks reveals that GP is able to match the accuracy of the network predictions but is more expensive computationally. However, a significant advantage of the GP algorithm is that it can automatically evolve the time history of model terms required to account for process characteristics such as the system time delay.The model development process is not simply a case of reducing the error between the predicted and actual process output. The parallel nature of GP means that it is ideally suited to solving multi-objective problems. The MBF-GP algorithm is modified to incorporate a Pareto based ranking scheme that allows models to be compared using multiple performance criteria. The ranking scheme allows preference information in the form of goals and priorities to be specified in order to guide the search towards the desired region of the search space. Two case studies are used to demonstrate the performance of this technique. The first example uses the multi-objective algorithm to improve the parsimony of the evolved model structures. The second example demonstrates how a set residual correlation tests can be combined and used as an additional performance measure. In each case, the multi-objective algorithm performs significantly better than the single objective version. In addition, the inclusion of preference information overcomes some of the difficulties associated with conventional Pareto ranking and produces a greater number of acceptable solutions. Prefaceii

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Section: -10mentioning

confidence: 92%

Section: Residual Analysismentioning

confidence: 99%

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Dynamic systems modelling using genetic programming

Hinchliffe¹,

Willis²

2003

Computers & Chemical Engineering

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“…Arkov et al employed four different system identification approaches to model a typical aircraft gas turbine using the obtained data from a twin-shaft Rolls Royce Spey engine (Arkov et al, 2000). The motivation behind their research was to minimize the cost and to improve the efficiency of gas turbine dynamical testing techniques.…”

Section: White-box Models Of Aero Gas Turbinesmentioning

confidence: 99%

Engineering Creative Design in Robotics and Mechatronics

Habib¹,

Davim²

2013

Advances in Mechatronics and Mechanical Engineering

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“…Torres et al [12] attempted to identify the dynamic of the gas turbine engine offline, mainly at steady states with stochastic signals. Arkov et al [13] focused on real-time identification for transient operations and concluded that an engine system could be averaged to a timeinvariant firstor second-order transfer function by the extended recursive least squares [13]. The tracking speed and accuracy for the recursive least squares could be improved with a different design of forgetting factors.…”

Section: Introductionmentioning

confidence: 99%

Study on Identification Method for Parameter Uncertainty Model of Aero Engine

Bai

Shuai

Wang

2019

International Journal of Aerospace Engineering

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The linear model of an aero engine is effective in a small range of the neighborhood of equilibrium points. According to this problem, the identification method for the parameter uncertain linear model of the aero engine was proposed. The identification problem is solved by calculating nonlinear programming. Considering the parameter uncertainty of the model is the critical point of this research during the optimization process. A parameter uncertain model of an aero engine can be obtained, which has large use range. This method is used for DGEN380 aero engine. The two parameters, VDD and VE, are defined for describing error range. Compared with experimental data, the uncertain model of DGEN 380 can simulate the real state of DGEN380 within 1% error range when ΔPLA < 22%. Compared with another conventional method of identification (recursive least squares), the parameter uncertain model, established by the method of this research, has a broad application area through parameter uncertainty of the model.

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System identification strategies applied to aircraft gas turbine engines

Cited by 8 publications

References 5 publications

Dynamic systems modelling using genetic programming

Dynamic systems modelling using genetic programming

Engineering Creative Design in Robotics and Mechatronics

Study on Identification Method for Parameter Uncertainty Model of Aero Engine

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