DOI QR코드

DOI QR Code

Information entropy based algorithm of sensor placement optimization for structural damage detection

  • Ye, S.Q. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University) ;
  • Ni, Y.Q. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University)
  • 투고 : 2012.02.22
  • 심사 : 2012.06.08
  • 발행 : 2012.10.25

초록

The structural health monitoring (SHM) benchmark study on optimal sensor placement problem for the instrumented Canton Tower has been launched. It follows the success of the modal identification and model updating for the Canton Tower in the previous benchmark study, and focuses on the optimal placement of vibration sensors (accelerometers) in the interest of bettering the SHM system. In this paper, the sensor placement problem for the Canton Tower and the benchmark model for this study are first detailed. Then an information entropy based sensor placement method with the purpose of damage detection is proposed and applied to the benchmark problem. The procedure that will be implemented for structural damage detection using the data obtained from the optimal sensor placement strategy is introduced and the information on structural damage is specified. The information entropy based method is applied to measure the uncertainties throughout the damage detection process with the use of the obtained data. Accordingly, a multi-objective optimal problem in terms of sensor placement is formulated. The optimal solution is determined as the one that provides equally most informative data for all objectives, and thus the data obtained is most informative for structural damage detection. To validate the effectiveness of the optimally determined sensor placement, damage detection is performed on different damage scenarios of the benchmark model using the noise-free and noise-corrupted measured information, respectively. The results show that in comparison with the existing in-service sensor deployment on the structure, the optimally determined one is capable of further enhancing the capability of damage detection.

키워드

참고문헌

  1. Beck, J.L. and Katafygiotis, L.S. (1998), "Updating models and their uncertainties I: Bayesian statistical framework", J. Eng. Mech.- ASCE, 124(4), 455-461. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455)
  2. Cobb, R.G. and Liebst, B.S. (1996), "Sensor location prioritization and structural damage localization using minimal sensor information", AIAA J., 35, 369-374.
  3. Doebling, S.W. (1995), Measurement of Structural Flexibility Matrices for Experiments with Incomplete Reciprocity, Ph.D. Dissertation, Colorado University, USA.
  4. Fonseca, C.M. and Flemming, P.J. (1995), "An overview of evolutionary algorithms in multi-objective optimization", Evolution. Comput., 13, 1-16.
  5. Guo, H.Y., Zhang, L., Zhang, L.L. and Zhou, J.X. (2004), "Optimal placement of sensors for structural health monitoring using improved genetic algorithms", Smart Mater. Struct., 13(3), 528-534. https://doi.org/10.1088/0964-1726/13/3/011
  6. Heo, G., Wang, M.L. and Satpathi, D. (1997), "Optimal transducer placement for health monitoring of long span bridge", Soil Dyn. Earthq. Eng., 16(7-8), 495-502. https://doi.org/10.1016/S0267-7261(97)00010-9
  7. Imamovic, N. (1998), Model validation of large finite element using test data, Ph.D. Dissertation, Imperial College London, UK.
  8. Kammer, D.C. (1991), "Sensor placement for on orbit modal identification and correlation of large space structures", J. Guid.Control Dynam., 14(2), 251-259. https://doi.org/10.2514/3.20635
  9. Ni ,Y.Q., Xia, Y., Liao, W.Y. and Ko, J.M. (2009), "Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower", Struct. Health Monit., 16(1), 73-98. https://doi.org/10.1002/stc.303
  10. Ni, Y.Q., Wong, K.Y. and Xia, Y. (2011), "Health checks through landmark bridges to sky-high structures", Adv. Struct. Eng., 14(1), 103-119. https://doi.org/10.1260/1369-4332.14.1.103
  11. Ni, Y.Q., Xia, Y., Lin, W., Chen, W.H. and Ko, J.M. (2012), "SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data", Smart Struct. Syst., in this issue.
  12. Meo, M. and Zumpano, G. (2005), "On the optimal sensor placement techniques for a bridge structure", Eng. Struct., 27(10),1488-1497. https://doi.org/10.1016/j.engstruct.2005.03.015
  13. Papadimitriou, C. (2004), "Optimal sensor placement methodology for parametric identification of structural systems", J. Sound Vib., 278(4-5), 923-947. https://doi.org/10.1016/j.jsv.2003.10.063
  14. Papadimitriou, C. (2005), "Pareto optimal sensor locations for structural identification", Comput. Method. Appl. M., 194(12-16), 1655-1673. https://doi.org/10.1016/j.cma.2004.06.043
  15. Rao, A.R.M. and Anadakumar, G. (2007), "Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique", Smart Mater. Struct., 16(6), 2658-2672. https://doi.org/10.1088/0964-1726/16/6/071
  16. Shi, Z.Y., Law, S.S. and Zhang, L.M. (2000a), "Damage localization by directly using incomplete mode shapes", J. Eng. Mech. - ASCE, 126(6), 656-660. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:6(656)
  17. Shi, Z.Y., Law, S.S. and Zhang, L.M. (2000b), "Optimum sensor placement for structural damage detection", J. Eng. Mech. - ASCE, 126(11), 1173-1179. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:11(1173)
  18. Souza, D.K. and Epureanu, B.I. (2008), "Sensor placement for damage detection in nonlinear systems augmentations", AIAA J., 46(10), 2434-2442. https://doi.org/10.2514/1.33493
  19. Srinivas, N. and Deb, K. (1994), "Multi-objective optimization using non-dominated sorting in genetic algorithms", Evolution. Comput., 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221
  20. Worden, K. and Burrows, A.P. (2001), "Optimal sensor placement for fault detection", Eng. Struct., 23(8), 885-901. https://doi.org/10.1016/S0141-0296(00)00118-8
  21. Yuen, K.V., Katafygiotis, L.S., Papadimitriou, C. and Mickleborough, N.C. (2001), "Optimal sensor placement methodology for identification with unmeasured excitation", J. Dyn. Syst. Measure. Control, 123(4), 677-686. https://doi.org/10.1115/1.1410929

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