Application of Integer-Coded Genetic Algorithm to Optimal Sensor Placement

Abstract:

Article Preview

In the dynamic testing, the sensor positions have a major influence on the quality of the experimental modal parameters of a tested structure. In order to dispose sensors to reasonable degrees of freedom (DOF), and reflect adequately the dynamic characteristics of tested structure, the sensor positions must be optimized. In this paper, taking the combination of MAC matrix and Fisher information matrix (FIM) as optimization criteria, the integer-coded genetic algorithm (IGA) was applied to optimal sensor position problem (OSPP). The effect of optimization criteria and optimal method to optimal sensor positions were discussed. According to the results, the following conclusion is obtained: using MAC and FIM as optimal criteria, introducing the IGA into the OSPP, the optimal sensor positions can ensure the better linear independence of the mode shape vectors and the better estimation of the experimental modal parameters. Comparing with three existing optimal sensor placement methods, which are Guyan, effective independence (EI), and cumulative method based on QR decomposition (CQRD), their results of the optimal sensor positions indicated that the IGA is better than them.

Info:

Periodical:

Advanced Materials Research (Volumes 271-273)

Edited by:

Junqiao Xiong

Pages:

1114-1119

DOI:

10.4028/www.scientific.net/AMR.271-273.1114

Citation:

B. Y. Qin and X. K. Lin, "Application of Integer-Coded Genetic Algorithm to Optimal Sensor Placement", Advanced Materials Research, Vols. 271-273, pp. 1114-1119, 2011

Online since:

July 2011

Export:

Price:

$35.00

[1] J. E. T. Penny, M. I. Friswell, and S. D. Garvey: Automatic choice of measurement positions for dynamic testing. AIAA Journal, vol. 32(2), pp.407-414, (1994).

DOI: 10.2514/3.11998

[2] Qin Boying, et al.: Optimal Sensor Placement Based on Integer-coded Genetic Algorithm. Journal of Vibration and Shock, vol. 30(2), pp.188-193, (2011).

[3] Yan Tianhong, et al.: Collocated sensor/actuator optimal positioning and feedback design by simulated annealing method. Journal of Vibration and Shock, vol. 19(2), pp.1-4, (2000).

[4] Zhang Hongwei, Xu Shijie, and Huang Wenhu: Global optimization of actuators/sensors placement using a float-encoding genetic algorithm. Journal of Vibration Engineering, vol. 12(4), pp.529-534, (1999).

[5] Huang Weiping, Liu Juan, and Li Huajun: Optimal sensor placement based on genetic algorithms. Engineering Mechanics, vol. 22(1), p.116, (2005).

[6] Lin Xian-kun, et al.: Application of coevolutionary genetic algorithm in optimal sensor placement. Journal of Vibration and Shock, vol. 28(3), pp.195-199, (2009).

[7] Z. Y. Shi, S. S. Law, and L. M. Zhang: Optimum sensor placement for structural damage detection. Journal of Engineering Mechanics, vol. 126(11), pp.1173-1179, (2000).

[8] Liu Juan, and Huang Weiping, Application of a cumulative method of sensor placement for offshore platforms. Journal of Ocean University of Qingdao, vol. 33(3), pp.476-482, (2003).

[9] Kammer: Sensor Placements for On-Orbit Modal Identification and Correlation of Large Space Structures. Journal of Guidance, Control and Dynamics, vol. 14(2), pp.251-259, (1991).

DOI: 10.2514/3.20635

[10] Qin Xianrong, et al.: Successive Sensor Placement for Modal Paring Based-on QR-Factorization. Journal of Vibration, Measurement & Diagnosis, vol. 21(3), pp.168-173, (2001).

[11] J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, MI, (1975).

[12] D. E. Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, (1989).

[13] Buthainah Fahran Al-Dulaimi, and Hamza A. Ali: Enhanced Traveling Salesman Problem Solving by Genetic Algorithm Technique. TSPGA, vol. 38, pp.296-302, (2008).

In order to see related information, you need to Login.