The Optimization of Balancing Least Squares Influence Coefficient Method Based on Particle Swarm Optimization

Xing Xing Wang; Guo An Yang; Ya Jun Fan

doi:10.4028/www.scientific.net/AMM.105-107.56

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 105-107The Optimization of Balancing Least Squares...

The Optimization of Balancing Least Squares Influence Coefficient Method Based on Particle Swarm Optimization

Abstract:

On the basis of the rotor dynamic balancing theory, the rotor balancing least squares influence coefficient method has been discussed in detail in this paper. In order to solve the problem that the residual vibration in some of the measuring point and the balancing weight are comparatively large in the balance process with least squares influence coefficient method, particle swarm optimization algorithm with cross-factor, which is an improved swarm intelligence algorithm, is introduced into rotor balancing least squares influence coefficient method. Theoretical analysis and numerical examples show that the algorithm has a good convergence, with reducing the balancing weight and residual vibration effectively compared with basic least squares influence coefficient method. The result can achieve better balance effect in the rotor balancing process.

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Periodical:

Applied Mechanics and Materials (Volumes 105-107)

Pages:

56-61

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.105-107.56

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Online since:

September 2011

Authors:

Xing Xing Wang, Guo An Yang, Ya Jun Fan

Keywords:

Influence Coefficient Method, Least-Square Method (LSM), Particle Swarm Optimization Algorithm (PSO), Rotor Balancing

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References

[1] X.N. Zhang, M. Zhao and G.R. Wen: China mechanical engineering, Vol. 21 (2010) No.16, p.1974. (In Chinese)

Google Scholar

[2] X.S. Wang: Journal of Xi'an Jiaotong University, (1998) No.11, p.76. (In Chinese)

Google Scholar

[3] J. Kennedy, R.C. Eberhart: IEEE Int, Lconf. on Neural Networks (IEEE Service Center, Piscataway, NJ,1995) Vol. 4 p.1942.

Google Scholar

[4] L.B. Deng, L. Cheng, Q.T. Li and N.D. Long: Mechanical science and technology. (2007) No.5, p.631. (In Chinese)

Google Scholar

[5] Goodman T P: ASME Transactions, Journal of Engineering for Industry, Vol. 86 (1964) No.3, p.273.

Google Scholar

[6] L.L. Wan, Z.J. Zhang, C.J. Li: Mechanical and electrical, (2010) No.4, p.21. (In Chinese)

Google Scholar

[7] S.R. Huang: (2009) No.8, p.1977. (In Chinese)

Google Scholar

[8] J. Li, X.X. Sun, S.B. Li and R. Li: Computer engineering, (2008) No.2, p.181. (In Chinese)

Google Scholar

[9] X.G. Gou, D.W. Zhang and Z.P. Zeng: Mechanical design, (2002) No.7, p.32. (In Chinese)

Google Scholar