Wyniki wyszukiwania - BazTech

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Formulation and identification of First-Principle Data-Driven models

Czop P., Kost G., Sławik D., Wszołek G.

Journal of Achievements in Materials and Manufacturing Engineering

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2011

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Vol. 44, nr 2

179--186

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.

2

Demonstration of First-Principle Data-Driven models using numerical case studies

Czop P., Sławik D., Wszołek G.

Journal of Achievements in Materials and Manufacturing Engineering

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2011

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Vol. 45, nr 2

170--177

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.

3

Estimation of feedwater heater parameters based on a grey-box approach

Barszcz T., Czop P.

International Journal of Applied Mathematics and Computer Science

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2011

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Vol. 21, no 4

703-715

EN

The first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink package. There are two common problems associated with the modeling of dynamic systems. If an analytical model is chosen, it is very costly to determine all model parameters and that often prevents this approach from being used. If a data model is chosen, one does not have a clear interpretation of the model parameters. The paper uses the so-called grey-box approach, which combines first-principle and data-driven models. The model is represented by nonlinear state-space equations with geometrical and physical parameters deduced from the available documentation of a feedwater heater, as well as adjustable phenomenological parameters (i.e., heat transfer coefficients) that are estimated from measurement data. The paper presents the background of the method, its implementation in the Matlab/Simulink environment, the results of parameter estimation, and a discussion concerning the accuracy of the method.

4

First-principle and data-driven model-based approach in rotating machinery failure mode detection

Wszołek G., Czop P.

Journal of Achievements in Materials and Manufacturing Engineering

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2010

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Vol. 43, nr 2

692--701

EN

Purpose: A major concern of modern diagnostics is the use of vibration or acoustic signals generated by a machine to reveal its operating conditions. This paper presents a method which allows to periodically obtain estimates of model eigenvalues represented by complex numbers. The method is intended to diagnose rotating machinery under transient conditions. Design/methodology/approach: The method uses a parametric data-driven model, the parameters of which are estimated using operational data. Findings: Experimental results were obtained with the use of a laboratory single-disc rotor system equipped with both sliding and hydrodynamic bearings. The test rig used allows measurements of data under normal, or reference, and malfunctioning operation, including oil instabilities, rub, looseness and unbalance, to be collected. Research limitations/implications: Numerical and experimental studies performed in order to validate the method are presented in the paper. Moreover, literature and industrial case studies are analyzed to better understand vibration modes of the rotor under abnormal operating conditions. Practical implications: A model of the test rig has been developed to verify the method proposed herein and to understand the results of the experiments. Hardware realization of the proposed method was implemented as a standalone operating module developed using the Texas Instruments TMS3200LF2407 Starter Kit. Originality/value: The parametric approach was proposed instead of nonparametric one towards diagnosing of rotating machinery.