DOI QR코드

DOI QR Code

A multitype sensor placement method for the modal estimation of structure

  • Pei, Xue-Yang (School of Civil Engineering, Dalian University of Technology) ;
  • Yi, Ting-Hua (School of Civil Engineering, Dalian University of Technology) ;
  • Li, Hong-Nan (School of Civil Engineering, Dalian University of Technology)
  • Received : 2017.10.04
  • Accepted : 2018.03.12
  • Published : 2018.04.25

Abstract

In structural health monitoring, it is meaningful to comprehensively utilize accelerometers and strain gauges to obtain the modal information of a structure. In this paper, a modal estimation theory is proposed, in which the displacement modes of the locations without accelerometers can be estimated by the strain modes of selected strain gauge measurements. A two-stage sensor placement method, in which strain gauges are placed together with triaxial accelerometers to obtain more structural displacement mode information, is proposed. In stage one, the initial accelerometer locations are determined through the combined use of the modal assurance criterion and the redundancy information. Due to various practical factors, however, accelerometers cannot be placed at some of the initial accelerometer locations; the displacement mode information of these locations are still in need and the locations without accelerometers are defined as estimated locations. In stage two, the displacement modes of the estimated locations are estimated based on the strain modes of the strain gauge locations, and the quality of the estimation is seen as a criterion to guide the selection of the strain gauge locations. Instead of simply placing a strain gauge at the midpoint of each beam element, the influence of different candidate strain gauge positions on the estimation of displacement modes is also studied. Finally, the modal assurance criterion is utilized to evaluate the performance of the obtained multitype sensor placement. A bridge benchmark structure is used for a numerical investigation to demonstrate the effectiveness of the proposed multitype sensor placement method.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

References

  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. Catbas, F.N., Gul, M., and Burkett, J.L. (2008). "Damage assessment using flexibility and flexibility-based curvature for structural health monitoring", Smart Mater. Struct., 17(1), 015024.
  3. Carne, T.G. and Dohrmann, C.R. (1995), "A modal test design strategy for model correlation", Proceedings-SPIE the International Society for Optical Engineering, Nashville, USA, Feb.
  4. Chang, M. and Pakzad, S.N. (2014), "Optimal sensor placement for modal identification of bridge systems considering number of sensing nodes", J. Bridge Eng.-ASCE, 19(6), 04014019.
  5. 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), 495-502. https://doi.org/10.1016/S0267-7261(97)00010-9
  6. Huang, H.B., Yi, T.H. and Li, H.N. (2017), "Bayesian combination of weighted principal component analysis for diagnosing sensor faults in structural monitoring systems", J. Eng. Mech.-ASCE, 143(9), 04017088.
  7. Johnson, R.A. and Wichern, D.W. (2002), Applied multivariate statistical analysis, Prentice Hall, 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. Kammer, D.C. and Tinker, M.L. (2004), "Optimal placement of triaxial accelerometers for modal vibration tests", Mech. Syst. Signal Pr., 18(1), 29-41. https://doi.org/10.1016/S0888-3270(03)00017-7
  10. Li, D.S., Li, H.N. and Fritzen, C.P. (2007), "The connection between effective independence and modal kinetic energy methods for sensor placement", J. Sound Vib., 305(4), 945-955. https://doi.org/10.1016/j.jsv.2007.05.004
  11. Li, J. and Hao, H. (2016), "A review of recent research advances on structural health monitoring in Western Australia", Struct. Monit. Maint., 3(1), 33-49. https://doi.org/10.12989/SMM.2016.3.1.033
  12. Li, J., Hao, H. and Chen, Z.W. (2017), "Damage identification and optimal sensor placement for structures under unknown traffic-induced vibrations", J. Aerosp. Eng.- ASCE, 30(2), B4015001.
  13. Papadimitriou, C., Beck, J.L. and Au, S.K. (2000), "Entropy-based optimal sensor location for structural model updating", J. Vib. Control, 6(5), 781-800. https://doi.org/10.1177/107754630000600508
  14. Papadimitriou, C. and Lombaert, G. (2012), "The effect of prediction error correlation on optimal sensor placement in structural dynamics", Mech. Syst. Signal Pr., 28(2), 105-127. https://doi.org/10.1016/j.ymssp.2011.05.019
  15. Papadopoulos, M. and Ephrahim, G. (1998), "Sensor placement methodologies for dynamic testing", AIAA J., 36(2), 256-263. https://doi.org/10.2514/2.7509
  16. Penny, J.E.T., Friswell, M.I. and Garvey, S.D. (1994), "Automatic choice of measurement locations for dynamic testing", AIAA J., 32(2), 407-414. https://doi.org/10.2514/3.11998
  17. Ren, L., Yuan, C.L., Li, H.N. and Yi, T.H. (2016), "Structural Health Monitoring System Developed for Dalian Stadium", Int. J. Struct. Stab. Dy., 16(4), 1640018.
  18. Shi, Z.Y., Law, S.S. and Zhang, L.M. (2000), "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)
  19. Stephan, C. (2012), "Sensor placement for modal identification", Mech. Syst. Signal Pr., 27, 461-470. https://doi.org/10.1016/j.ymssp.2011.07.022
  20. Udwadia, F.E. (1994), "Methodology for optimum sensor locations for parameter identification in dynamic systems", J. Eng. Mech.-ASCE, 120(2), 368-390. https://doi.org/10.1061/(ASCE)0733-9399(1994)120:2(368)
  21. 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
  22. Yam, L.Y., Leung, T.P., Li, D.B. and Xue, K.Z. (1996), "Theoretical and experimental study of modal strain analysis", J. Sound Vib., 191(2), 251-260. https://doi.org/10.1006/jsvi.1996.0119
  23. Yi, T.H., Li, H.N. and Gu, M. (2012a), "Sensor placement for structural health monitoring of Canton Tower", Smart Struct. Syst., 10(4-5), 313-329. https://doi.org/10.12989/sss.2012.10.4_5.313
  24. Yi, T.H., Li, H.N. and Zhang, X.D. (2012b), "Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm", Smart Mater. Struct., 21(12), 125023.
  25. Yi, T.H., Li, H.N. and Wang, C.W. (2015a), "Multiaxial sensor placement optimization in structural health monitoring using distributed wolf algorithm", Struct. Control. Health., 23(4), 719-734.
  26. Yi, T.H., Zhou, G.D., Li, H.N. and Zhang, X.D. (2015b), "Optimal sensor placement for health monitoring of high-rise structure based on collaborative-climb monkey algorithm", Struct. Eng. Mech., 54(2), 305-317. https://doi.org/10.12989/sem.2015.54.2.305
  27. Yuen, K.V. and Kuok, S.C. (2015), "Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi-type sensory systems", Earthq. Eng. Struct. D., 44(5), 757-774. https://doi.org/10.1002/eqe.2486
  28. Zhang, X.H, Xu, Y.L., Zhu, S. and Zhan, S. (2014), "Dual-type sensor placement for multi-scale response reconstruction", Mechatronics, 24(4), 376-384. https://doi.org/10.1016/j.mechatronics.2013.05.007
  29. Zhang, C.D. and Xu, Y.L. (2016), "Optimal multi-type sensor placement for response and excitation reconstruction", J. Sound Vib., 360, 112-128. https://doi.org/10.1016/j.jsv.2015.09.018

Cited by

  1. Pareto-Based Bi-Objective Optimization Method of Sensor Placement in Structural Health Monitoring vol.11, pp.11, 2018, https://doi.org/10.3390/buildings11110549