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A GRU-KAN Surrogate Model with Genetic Algorithm Uniform Sampling for Active Magnetic Bearings-Rotor Critical Speed

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A new hybrid GRU-KAN model rapidly and accurately predicts active magnetic bearing (AMB) rotor dynamics, overcoming limitations of traditional finite element modeling (FEM) for high-speed applications.

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AMB–rotorGRU-KANcritical speeddynamic modelsurrogate model

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Area of Science:

  • Mechanical Engineering
  • Control Systems
  • Machine Learning

Background:

  • Active magnetic bearings (AMBs) are crucial for high-speed rotating machinery.
  • Understanding rotor dynamics is vital for AMB performance and stability.
  • Traditional finite element modeling (FEM) is computationally intensive for analyzing controller parameter effects on dynamics.

Purpose of the Study:

  • To develop a rapid and accurate surrogate model for high-speed rotor dynamics in AMB systems.
  • To investigate the influence of coupled control gains on rotor dynamic behavior.
  • To provide an efficient alternative to traditional FEM for critical speed estimation.

Main Methods:

  • A hybrid gated recurrent unit-Kolmogorov-Arnold network (GRU-KAN) surrogate model was developed.
  • A genetic algorithm-driven uniform design sampling strategy was employed for model generalization.
  • The GRU-KAN model was compared against support vector regression and Kriging surrogates.

Main Results:

  • The GRU-KAN surrogate model achieved a high coefficient of determination (R2=0.9887) and low residuals.
  • Experimental validation showed mean absolute error of 38.51 rpm for critical speed prediction.
  • The surrogate model evaluation time was significantly reduced to 1.14×10-4 s compared to 201 s for FEM.

Conclusions:

  • The proposed GRU-KAN surrogate model offers high efficiency, accuracy, and comprehensive predictive capabilities.
  • This approach enables rapid critical speed estimation in AMB-rotor systems.
  • The findings facilitate optimized design and control of high-speed AMB systems.