caa a22 4500 463244117 CHVBK 20180405153320.0 cr unu---uuuuu 170326e20070601xx s 000 0 eng 10.1007/s00158-006-0055-5 doi (NATIONALLICENCE)springer-10.1007/s00158-006-0055-5 Bearing parameter identification of rotor-bearing system using clustering-based hybrid evolutionary algorithm [Elektronische Daten] [Yong-Han Kim, Bo-Suk Yang, Andy Tan] A new bearing parameter identification methodology based on global optimization scheme using measured unbalance response of rotor-bearing system is proposed. A new hybrid evolutionary algorithm which is a clustering-based hybrid evolutionary algorithm (CHEA), is proposed for global optimization scheme to improve the convergence speed and global search ability. Clustering of individuals by using a neural network is introduced to evaluate the degree of mature of genetic evolution. After clustering-based genetic algorithm (GA), local search is carried out for each cluster to judge the convexity of each cluster. Finally, random search is adapted for extrasearching to find a potential global candidate, which could be missed in GA and local search. The proposed methodology can identify not only unknown bearing parameters but also unbalance information of disk by simply setting them as unknown parameters. Numerical example and experimental results were used to verify the effectiveness of the proposed methodology. Springer-Verlag Berlin Heidelberg, 2006 Clustering-based hybrid evolutionary algorithm nationallicence Optimization nationallicence Identification nationallicence Rotor-bearing system nationallicence Bearing parameter nationallicence Kim Yong-Han School of Engineering Systems, Queensland University of Technology, GPO Box 2343, 4001, Brisbane, QLD, Australia aut Yang Bo-Suk School of Mechanical Engineering, Pukong National University, San 100, Yongdang-dong, Nam-gu, 608-739, Busan, Republic of Korea aut Tan Andy School of Engineering Systems, Queensland University of Technology, GPO Box 2343, 4001, Brisbane, QLD, Australia aut Structural and Multidisciplinary Optimization Springer-Verlag 33/6(2007-06-01), 493-506 1615-147X 33:6<493 2007 33 158 https://doi.org/10.1007/s00158-006-0055-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00158-006-0055-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kim Yong-Han School of Engineering Systems, Queensland University of Technology, GPO Box 2343, 4001, Brisbane, QLD, Australia aut NATIONALLICENCE 700 1- Yang Bo-Suk School of Mechanical Engineering, Pukong National University, San 100, Yongdang-dong, Nam-gu, 608-739, Busan, Republic of Korea aut NATIONALLICENCE 700 1- Tan Andy School of Engineering Systems, Queensland University of Technology, GPO Box 2343, 4001, Brisbane, QLD, Australia aut NATIONALLICENCE 773 0- Structural and Multidisciplinary Optimization Springer-Verlag 33/6(2007-06-01), 493-506 1615-147X 33:6<493 2007 33 158 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer