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Formulation and identification of First-Principle Data-Driven models

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose The paper consists of two parts. The first part presents and discusses a process of formulation and identification of First-Principle Data-Driven (FPDD) models, while the second part demonstrates numerical examples of identification of FPDD models. Design/methodology/approach: First-Principle (FP) model is formulated using a system of continuous ordinary differential equations capturing usually nonlinear relations among variables of the model. The considering model applies three categories of parameters: geometrical, physical and phenomenological. Geometrical and physical parameters are deduced from construction or operational documentation. The phenomenological parameters are the adjustable ones, which are estimated or adjusted based on their roughly known values, e.g. friction/damping coefficients. Findings A few phenomenological parameters were successfully estimated from numerically generated data. The error between the true and estimated value of the parameter occurred, however its magnitude is low at level below 2%. Research limitations/implications Adjusting a model to data is, in most cases, a non-convex optimization problem and the criterion function may have several local minima. This is a case when multiple parameters are simultaneously estimated. Practical implications: FPDD models are an excellent tool for understanding, optimizing, designing, and diagnosing technical systems since they are updatable using operational measurements. This opens application area, for example, for model-based design and early warning diagnostics. Originality/value: First-Principle (FP) models are frequently adjusted by trial-and-error, which can lead to non-optimal results. In order to avoid deficiencies of the trial-and-error approach, a formalized mathematical method using optimization techniques to minimize the error criterion, and find optimal values of tunable model parameters, was proposed and demonstrated in this work.
Rocznik
Strony
179--186
Opis fizyczny
Bibliogr. 27 poz..
Twórcy
autor
  • Tenneco Automotive Eastern Europe, Eastern European Engineering Center (EEEC), ul. Bojkowska 59 B, 44-100 Gliwice, Poland
autor
  • Institute of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
autor
  • Tenneco Automotive Eastern Europe, Eastern European Engineering Center (EEEC), ul. Bojkowska 59 B, 44-100 Gliwice, Poland
autor
  • Institute of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
Bibliografia
  • [1] K. Białas, Synthesis of mechanical systems including passive or active elements reducing of yibrations, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 323-326.
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  • [4] A. Naddeo, M. Annarumma, M. Pappalardo, N. Cappetti, State of the art of the passive pedestrian safety simulation, Journal of Achievements in Materials and Manufacturing Engineering, 30/1 (2008) 51-58.
  • [5] S. Żółkiewski, Analysis and modelling of rotational systems with the Modyfit application, Journal of Achievements in Materials and Manufacturing Engineering 30/1 (2008) 59-66
  • [6] K. Białas, Comparison of passive and active reduction of vibrations of mechanical systems, Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 455-458.
  • [7] A. Buchacz, Influence of piezoelectric on characteristics of vibrating mechatronical system, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 229-232.
  • [8] A. Buchacz, S. Żółkiewski, Dynamic analysis of the mechanical systems vibrating transversally in transportation, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 331-334.
  • [9] J. Świder, G. Wszołek, K. Foit, P. Michalski, S. Jendrysik, Example of the analysis of mechanical system vibrations in GRAFSIM and CATGEN software, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 391-394.
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  • [16] K. Li, S. Thompson, Developing a NO, emission model for a coal-fired power generation plant using artificial neural networks. UKACC International Conference on Control, Cambridge, 2000, 347-352.
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  • [23] G. Ferretti, L. Piroddi, Estimation of NO, emissions in thermal power plants using neural networks, ASME Journal of Engineering for Gas Turbine and Power 123 (2001) 465-471.
  • [24] K.C. Tan, Y. Li, Grey-box model identification via evolutionary computing. Control Engineering Practice 10/7 (2002) 673-684.
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Typ dokumentu
Bibliografia
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