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.

Keywords

• turbine rotor
• vibration behavior
• prediction
• geometric deflections
• imbalance
• cae system
• neural network