Algorithm for predicting the vibrational state of a turbine rotor using machine learning

Abstract


A machine learning algorithm has been developed to solve the problem of predicting a vibrational state in order to improve the turbine rotor assembly processes using its digital twin. The digital twin of the rotor includes a parametric 3D model specially created in the CAD module of the NX program and a design project in the ANSYS system in which the working conditions of the rotor are simulated. The parameters of vibration acceleration and the reaction force of the rotor supports at critical speeds depending on geometric errors were calculated. To reduce the complexity of the calculations, neural network architectures were chosen to predict the parameters of the vibrational state depending on the geometric errors of the rotors. The novelty of the work lies in the creation and use of the original numerical model of balancing, taking into account the rotor manufacturing tolerances.


About the authors

M. A. Bolotov

Samara National Research University

Author for correspondence.
Email: maikl.bol@gmail.com
ORCID iD: 0000-0003-2653-0782

Russian Federation

Candidate of Science (Engineering),
Associate Professor of the Department of Engine Production Technology

V. A. Pechenin

Samara National Research University

Email: vadim.pechenin2011@yandex.ru
ORCID iD: 0000-0003-4961-7338

Russian Federation

Candidate of Science (Engineering),
Associate Professor of the Department of Engine Production Technology

E. J. Pechenina

Samara National Research University

Email: ek-ko@list.ru

Russian Federation

Postgraduate student of the Department of Engine Production Technology

N. V. Ruzanov

Samara National Research University

Email: kinform_@mail.ru
ORCID iD: 0000-0001-8086-0884

Russian Federation

Lead Programmer of the Department of Engine Production Technology

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