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Application of Artificial Neural Network for Identification of Bearing Stiffness Characteristics in Rotor Dynamics Analysis

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Advances in Design, Simulation and Manufacturing (DSMIE 2019)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

In this article the implementation of the mathematical model for rotor oscillations on non-linear bearing supports for the multistage centrifugal compressor is considered by using the computer program “Critical frequencies of the rotor”. It realized the finite element mathematical model, which allows taking into account the non-linear dependence of bearing stiffness on the rotor speed, as well as gyroscopic moments of inertia of impellers and shell-type parts. The artificial neural network “Virtual Gene Developer” software is proposed for evaluating the operating parameters of the approximating curve “bearing stiffness – rotor speed” by the dataset of numerical simulation results in the abovementioned software. Actual parameters of non-linear bearing stiffness are obtained by the results of the experimental research of rotor critical frequencies for the multistage centrifugal compressor 295GC2-190/44-100M on the experimental accelerating-balancing stand “Schenck”. The main advantages of the proposed approach and methodology for application of Artificial Neural Networks are stated.

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Acknowledgements

This work was supported by the Slovak Research and Development Agency under the contract No. APVV-15-0602.

The main scientific results were achieved within The National Scholarship Programme of the Slovak Republic at the Faculty of Manufacturing Technologies with a seat in Prešov of Technical University of Košice (Prešov, Slovakia) and recent research at Sumy State University: No. 0117U004922 and No. 0117U003931.

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Authors and Affiliations

  1. Sumy State University, 2 Rymskogo-Korsakova Street, Sumy, 40007, Ukraine

    Ivan Pavlenko, Vitalii Simonovskiy & Vitalii Ivanov

  2. Technical University of Kosice, 1 Bayerova Street, 08001, Presov, Slovak Republic

    Jozef Zajac & Jan Pitel

Authors
  1. Ivan Pavlenko
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  2. Vitalii Simonovskiy
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  3. Vitalii Ivanov
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  4. Jozef Zajac
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  5. Jan Pitel
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Corresponding author

Correspondence to Ivan Pavlenko .

Editor information

Editors and Affiliations

  1. Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine

    Vitalii Ivanov

  2. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

    Yiming Rong

  3. Department of Manufacturing and Production Engineering, Poznan University of Technology, Poznan, Poland

    Justyna Trojanowska

  4. Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Brandenburg, Germany

    Joachim Venus

  5. Department of Processes and Equipment of Chemical and Petroleum-Refineries, Sumy State University, Sumy, Ukraine

    Oleksandr Liaposhchenko

  6. Faculty of Manufacturing Technologies with a seat in Presov, Technical University of Kosice, Prešov, Slovakia

    Jozef Zajac

  7. Department of General Mechanics and Machine Dynamics, Sumy State University, Sumy, Ukraine

    Ivan Pavlenko

  8. Faculty of Mechanical Engineering, University of West Bohemia, Pilsen, Czech Republic

    Milan Edl

  9. Department of Information and Communication Traffic, University of Zagreb, Zagreb, Croatia

    Dragan Perakovic

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Pavlenko, I., Simonovskiy, V., Ivanov, V., Zajac, J., Pitel, J. (2019). Application of Artificial Neural Network for Identification of Bearing Stiffness Characteristics in Rotor Dynamics Analysis. In: Ivanov, V., et al. Advances in Design, Simulation and Manufacturing. DSMIE 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-93587-4_34

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  • DOI: https://doi.org/10.1007/978-3-319-93587-4_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93586-7

  • Online ISBN: 978-3-319-93587-4

  • eBook Packages: EngineeringEngineering (R0)

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