Abstract
Based on the Holo-balancing theory of shaft system, a new multi-objective optimization balancing method including load mass, uniformity and maximum of the residual vibration is proposed by building a multi-objective fuzzy evaluation function and application of particle swarm optimization algorithm. The advantage of the proposed method is studied by comparing with the traditional genetic algorithms optimization. And the shortcoming of influence coefficient methods failing to restrict the load mass and guaranteeing the uniformity of the residual vibration is conquered. Finally, the validity and effectiveness of the proposed method is verified through a field power generator set balancing case.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wu, S.T.: Study on field balancing method of flexible rotor sets. Master thesis of Xi’an Jiaotong University (2003)
Grobel, L.P.: Balancing turbine generator rotors. General electric review 56(4), 22 (1953)
Goodman, T.P.: A least-squares method for computing balance corrections. ASME Transactions. Journal of engineering for industry 86(3), 273–279 (1964)
Qu, L.S., Liu, X., Chen, Y.D.: Discovering the holospectrum. Noise & Vibration Control Worldwide 20(2), 58–62 (1989)
Qu, L.S., Liu, X., Peyronne, G., Chen, Y.D.: The holospectrum: a new method for rotor surveillance and diagbosis. Mechanical Systems and Signal Processing 3(3), 255–267 (1989)
Qu, L.S., Chen, Y.D., Liu, J.Y.: The holospectrum: a new FFT based rotor diagnostic method. In: Proceedings of the 1st International Machinery Monitoring & Diagnostics Conference, Las Vegas, Nevada, vol. 9, pp. 196–201 (1989)
Qu, L.S., Qiu, H., Xu, G.H.: Rotor balancing based on Holospectrum analysis: principle and practice. China mechanical engineering 9(1), 60–63 (1998)
Qu, L.S., Wang, X.F.: An introduction to Holo-balancing technique. Shaanxi Electric Power 1, 1–5 (2007)
Jia, Z.H.H., Chen, H.P., Sun, Y.H.: Multi-objective Particle Swarm Optimization Algorithm for Flexible Job Shop Scheduling. Journal of Chinese Computer Systems 29(5), 885–889 (2008)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press, New Jersey (1995)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Kim, J.H., Zhang, B.-T., Fogel, G., Kuscu, I. (eds.) Proceedings of the 2001 IEEE Congress on Evolutionary Computation, pp. 81–86. IEEE Press, New Jersey (2001)
Yang, W., Li, Q.Q.: Survey on Particle Swarm Optimization Algorithm. Engineering Science 16(15), 87–94 (2004)
Li, J.Q., Shi, G.Z.: A Study of the Relationship of Crossover Rate and Mutation Rate in Genetic Algorithm. Journal of Wuhan University of Technology 27(1), 97–99 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wen, G., Zhang, X., Zhao, M. (2010). Application of Partical Swarm Optimization Algorithm in Field Holo-Balancing . In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15621-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-15621-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15620-5
Online ISBN: 978-3-642-15621-2
eBook Packages: Computer ScienceComputer Science (R0)