Hostname: page-component-89b8bd64d-5bvrz Total loading time: 0 Render date: 2026-05-10T00:51:47.898Z Has data issue: false hasContentIssue false
  • English
  • Français

Sensor Configuration Optimizing in Modal Identification by Siege ant Colony Algorithm

Published online by Cambridge University Press:  19 September 2016

S. Feng ,
J.-Q. Jia  and
J.-C. Zhang
S. Feng*
Affiliation:
State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian, China
J.-Q. Jia
Affiliation:
State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian, China
J.-C. Zhang
Affiliation:
State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian, China
*
*Corresponding author (fengshuotougao@126.com)
Get access

Abstract

Proper monitor planning is a vital component of structural health monitoring (SHM) project. An extremely important part of the monitor planning is the placement of sensors, usually in the form of acceleration sensors. For the placement of three-dimensional acceleration sensors, the state of practice is to select the sensor configuration by previous experiences. However, this results in a waste of many sensors. A novel method called siege ant colony algorithm (SAC) is proposed in this paper. This method is built on the previous ant colony optimization (ACO) in the direction of improving efficiency and accuracy when applied to optimal sensor placement (OSP) problems in large-scale structure monitoring. This method is applied and compared with standard approaches using the Hanjiang transmission tower.

Keywords

Structural health monitoringModal identificationOptimal sensor placementSiege ant colony algorithm

Information

Type
Research Article
Information
Journal of Mechanics , Volume 33 , Issue 2 , April 2017 , pp. 269 - 277
Copyright
Copyright © The Society of Theoretical and Applied Mechanics 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1. Giurgiutiu, V., “Tuned Lamb Wave Excitation and Detection with Piezoelectric Wafer Active Sensors for Structural Health Monitoring,” Journal of Intelligent Material Systems and Structures, 16, pp. 291–305 (2005).Google Scholar
2. Farrar, C. R. and Worden, K., “An Introduction to Structural Health Monitoring,” Philosophical Transactions of the Royal Society a-Mathematical Physical and Engineering Sciences, 365, pp. 303–315 (2007).Google Scholar
3. Chen, C. H., Shen, Y. L. and Shin, C. S., “Using Distributed Brillouin Fiber Sensor to Detect the Strain and Cracks of Steel Structures,” Journal of Mechanics, 26, pp. 547–551 (2010).Google Scholar
4. Lynch, J. P. and Loh, K. J., “A Summary Review of Wireless Sensors and Sensor Networks for Structural Health Monitoring,” Shock and Vibration Digest, 38, pp. 91–128 (2006).Google Scholar
5. Wan, Z., Li, J. D., Jia, M. and Li, J. L., “Structural Health Monitoring (Shm) of Three-Dimensional Braided Composite Material Using Carbon Nanotube Thread Sensors,” Journal of Mechanics, 29, pp. 617–621 (2013).Google Scholar
6. Raghavan, A. and Cesnik, C. E. S., “Review of Guided-Wave Structural Health Monitoring,” Shock and Vibration Digest, 39, pp. 91–114 (2007).CrossRefGoogle Scholar
7. Harasawa, S. and Sato, M., “Helical Buckling of Slender Beam Structures Surrounded by an Elastic Medium,” Journal of Mechanics, 31, pp. 241–247 (2015).CrossRefGoogle Scholar
8. Wirowski, A., Michalak, B. and Gajdzicki, M., “Dynamic Modelling of Annular Plates of Functionally Graded Structure Resting on Elastic Heterogeneous Foundation with Two Modules,” Journal of Mechanics, 31, pp. 493–504 (2015).Google Scholar
9. Yam, L. H., Yan, Y. J. and Jiang, J. S., “Vibration-Based Damage Detection for Composite Structures Using Wavelet Transform and Neural Network Identification,” Composite Structures, 60, pp. 403–412 (2003).CrossRefGoogle Scholar
10. Salama, M., Rose, T. and Garba, J., “Optimal Placement of Excitations and Sensors for Verification of Large Dynamical Systems,” Proceedings of the 28th Structures, Structural Dynamics, and Materials Conference, pp. 6–8 (1987).Google Scholar
11. Henshell, R. and Ong, J., “Automatic Masters for Eigenvalue Economization,” Earthquake Engineering & Structural Dynamics, 3, pp. 375–383 (1974).Google Scholar
12. Kammer, D. C., “Sensor Placement for on-Orbit Modal Identification and Correlation of Large Space Structures,” Journal of Guidance, Control, and Dynamics, 14, pp. 251–259 (1991).CrossRefGoogle Scholar
13. Kammer, D. C. and Tinker, M. L., “Optimal Placement of Triaxial Accelerometers for Modal Vibration Tests,” Mechanical Systems and Signal Processing, 18, pp. 29–41 (2004).Google Scholar
14. Batou, A., “Model Updating in Structural Dynamics—Uncertainties on the Position and Orientation of Sensors and Actuators,” Journal of Sound and Vibration, 354, pp. 47–64 (2015).Google Scholar
15. Bishop, A. N., Fidan, B., Anderson, B. D., Doğançay, K. and Pathirana, P. N., “Optimality Analysis of Sensor-Target Localization Geometries,” Automatica, 46, pp. 479–492 (2010).CrossRefGoogle Scholar
16. Guo, H., Zhang, L., Zhang, L. and Zhou, J., “Optimal Placement of Sensors for Structural Health Monitoring Using Improved Genetic Algorithms,” Smart Materials and Structures, 13, pp. 528 (2004).Google Scholar
17. Han, J.-H. and Lee, I., “Optimal Placement of Piezoelectric Sensors and Actuators for Vibration Control of a Composite Plate Using Genetic Algorithms,” Smart Materials and Structures, 8, pp. 257 (1999).Google Scholar
18. Herzog, R. and Riedel, I., “Sequentially Optimal Sensor Placement in Thermoelastic Models for Real Time Applications,” Optimization and Engineering, pp. 1–30 (2014).Google Scholar
19. Joshi, S. and Boyd, S., “Sensor Selection Via Convex Optimization,” IEEE Transactions on Signal Processing, 57, pp. 451–462 (2009).CrossRefGoogle Scholar
20. Krause, A., Singh, A. and Guestrin, C., “Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies,” The Journal of Machine Learning Research, 9, pp. 235–284 (2008).Google Scholar
21. Dorigo, M., Maniezzo, V. and Colorni, A., “Ant System: Optimization by a Colony of Cooperating Agents,” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26, pp. 29–41 (1996).Google Scholar
22. MATLAB 2007, MathWorks, Natick, Massachusetts, U.S.Google Scholar
23. Carne, T. G. and Dohrmann, C., “A Modal Test Design Strategy for Model Correlation,” Proceedings-Spie The International Society For Optical Engineering, pp. 927–927 (1995).Google Scholar
24. SAP2000, Computers and Structures, Inc., Berkeley, California, U.S.Google Scholar
25. Yi, T. H., Li, H. N. and Gu, M., “Optimal Sensor Placement for Structural Health Monitoring Based on Multiple Optimization Strategies,” The Structural Design of Tall and Special Buildings, 20, pp. 881–900 (2011).Google Scholar
26. Yi, T.-H., Li, H.-N. and Gu, M., “Optimal Sensor Placement for Health Monitoring of High-Rise Structure Based on Genetic Algorithm,” Mathematical Problems in Engineering, 2011, pp. 1–12 (2011).Google Scholar