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Title: Balancing of a rigid rotor using artificial neural network to predict the correction masses
Other Titles: Balanceamento de um rotor rígido, usando redes neurais artificiais para a predição das massas de correção
Keywords: Redes neurais artificiais
Rigid balancing
Rotor balancing
Artificial neural network
Balanceamento rígido
Balanceamento de rotor
Issue Date: 2009
Publisher: Editora da Universidade Estadual de Maringá-Eduem
Citation: SANTOS, F. L. et al. Balancing of a rigid rotor using artificial neural network to predict the correction masses. Acta Scientiarum. Technology, Maringá, v. 31, n. 2, p. 151-157, 2009.
Abstract: This paper deals with an analytical model of a rigid rotor supported by hydrodynamic journal bearings where the plane separation technique together with the Artificial Neural Network (ANN) is used to predict the location and magnitude of the correction masses for balancing the rotor bearing system. The rotating system is modeled by applying the rigid shaft Stodola-Green model, in which the shaft gyroscopic moments and rotatory inertia are accounted for, in conjunction with the hydrodynamic cylindrical journal bearing model based on the classical Reynolds equation. A linearized perturbation procedure is employed to render the lubrication equations from the Reynolds equation, which allows predicting the eight linear force coefficients associated with the bearing direct and cross-coupled stiffness and damping coefficients. The results show that the methodology presented is efficient for balancing rotor systems. This paper gives a step further in the monitoring process, since Artificial Neural Network is normally used to predict, not to correct the mass unbalance. The procedure presented can be used in turbo machinery industry to balance rotating machinery that require continuous inspections. Some simulated results will be used in order to clarify the methodology presented.
Appears in Collections:DEG - Artigos publicados em periódicos

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