Open Access
Mechanics & Industry
Volume 16, Number 4, 2015
Article Number 406
Number of page(s) 9
Published online 12 June 2015
  1. M.J. Goodwin, Dynamics of Rotor-Bearing Systems, Unwin Hyman Ltd., London, 1989 [Google Scholar]
  2. S. Liu, A modified low-speed balancing method for flexible rotors based on holospectrum, Mech. Syst. Signal Process. 21 (2007) 348−364 [CrossRef] [Google Scholar]
  3. S.G. Tan, X.X. Wang, A Theoretical Introduction to Low Speed Balancing of Flexible Rotors: Unification and Development of the Modal Balancing and Influence Coefficient Techniques, J. Sound Vib. 168 (1993) 385−394 [CrossRef] [Google Scholar]
  4. S. Liu, L. Qu, A new field balancing method of rotor systems based on holospectrum and genetic algorithm, Appl. Soft Comput. 8 (2008) 446−455 [CrossRef] [Google Scholar]
  5. W. Victor, Machinery Vibration: Balancing, McGraw-Hill, Inc., USA, 1995 [Google Scholar]
  6. B. Xu, L. Qu, R. Sun, The Optimization Technique-Based Balancing of Flexible Rotors without Test Runs, J. Sound Vib. 238 (2000) 877−892 [CrossRef] [Google Scholar]
  7. T. Yamamoto, Y. Ishida, Linear and Nonlinear Rotordynamics: A Modern Treatment with Applications, John Wiley & Sons, Inc.. New York, 2001 [Google Scholar]
  8. A.S. Das, M.C. Nighil, J.K. Dutt, H. Irretier, Vibration control and stability analysis of rotor-shaft system with electromagnetic exciters, Mech. Mach. Theory 43 (2008) 1295−1316 [CrossRef] [Google Scholar]
  9. Y. Kang, T.-W. Lin, Y.-J. Chang, Y.-P. Chang, C.-C. Wang, Optimal balancing of flexible rotors by minimizing the condition number of influence coefficients, Mech. Mach. Theory 43 (2008) 891−908 [CrossRef] [Google Scholar]
  10. ANSYS, Ansys Structural Analysis Guide: ANSYS Release 10.0, in, USA, 2005 [Google Scholar]
  11. A.P. Engelbrecht, Computational Intelligence An Introduction, 2nd edition, John Wiley & Sons, Ltd, England, 2007 [Google Scholar]
  12. D. Vasiljevic′, J. Golobic′, Comparison of the classical dumped least squares and genetic algorithm in the optimization of the doublet, First Online Workshop on Soft Computing (1996) 200−204 [Google Scholar]
  13. T.T. Luong, Q.T. Pham, A Comparison Of The Performance Of Classical Methods and Genetic Algorithms For Optimization Problems Involving Numerical Models, IEEE (2003) [Google Scholar]
  14. A.M. Anile, V. Cutello, G. Nicosia, R. Rascuna‘, S. Spinella, Comparison among Evolutionary Algorithms and Classical Optimization Methods for Circuit Design Problems, IEEE (2005) 765−772 [Google Scholar]
  15. R. Storn, K. Price, Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Global Optimization 11 (1997) 341−359 [CrossRef] [MathSciNet] [Google Scholar]
  16. A.A. Abou El Ela, M.A. Abido, S.R. Spea, Differential evolution algorithm for emission constrained economic power dispatch problem, Elect. Power Syst. Res. 80 (2010) 1286–1292 [CrossRef] [Google Scholar]
  17. M.A. Abido, Parameter optimization of multimachine power system stabilizers using genetic local search, Int. J. Electrical Power Energy Syst. 23 (2001) 785−794 [CrossRef] [Google Scholar]
  18. R. Chiong, O.K. Beng, A Comparison between Genetic Algorithms and Evolutionary Programming based on Cutting Stock Problem, Eng. Lett. 14 (2007) EL14114 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.