Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Implementation of a bio-inspired two-mode structural health monitoring system

  • Lin, Tzu-Kang (National Center for Research on Earthquake Engineering) ;
  • Yu, Li-Chen (Department of Civil Engineering, National Taiwan University) ;
  • Ku, Chang-Hung (Department of Civil Engineering, National Taiwan University) ;
  • Chang, Kuo-Chun (Department of Civil Engineering, National Taiwan University) ;
  • Kiremidjian, Anne (Department of Civil and Environmental Engineering, Stanford University)
  • Received : 2010.04.18
  • Accepted : 2010.10.20
  • Published : 2011.07.25
⟨ Previous Next ⟩

Abstract

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

Keywords

References

  1. Hickman, G.A., Gerardi, J.J. and Feng, Y. (1991), "Application of smart structures to aircraft health monitoring", J. Intel. Mat. Syst. Str., 2(3), 411-430. https://doi.org/10.1177/1045389X9100200308
  2. Aktan, A.E., Helmicki, A.J. and Hunt, V.J. (1998), "Issues in health monitoring for intelligent infrastructure", Smart Mater. Struct., 7(5), 674-92. https://doi.org/10.1088/0964-1726/7/5/011
  3. Pines, D.J. and Lovell, P.A. (1998), "Conceptual framework of a remote wireless health monitoring system for large civil structures", Smart Mater. Struct., 7(5), 627-36. https://doi.org/10.1088/0964-1726/7/5/007
  4. Tanner, N.A., Wait, J.R., Farrar, C.R. and Hoon, S. (2003), "Structural health monitoring using modular wireless sensors", J. Intel. Mat. Syst. Struct., 14(1), 43-56. https://doi.org/10.1177/1045389X03014001005
  5. Wang, Y., Lynch, J.P. and Law, K.H. (2007), "A wireless structural health monitoring system with multithreaded sensing devices: Design and validation", Struct. Infrastruct. E., 3(2), 103-120. https://doi.org/10.1080/15732470600590820
  6. Lynch, J.P., Law, K.H., Kiremidjian, A.S., Carryer, E., Farrar, C.R., Sohn, H., Allen, D.W., Nadler, B. and Wait, J.R. (2004), "Design and performance validation of a wireless sensing unit for structural monitoring applications", Struct. Eng. Mech., 17(3-4), 393-408. https://doi.org/10.12989/sem.2004.17.3_4.393
  7. Buckner, B.D., Markov, V., Li-Chung, L. and Earthman, J.C. (2008), "Laser-scanning structural health monitoring with wireless sensor motes", Opt. Eng., 47(5), 1-9.
  8. Zimmerman, A.T., Shiraishi, M., Swartz, R.A. and Lynch, J.P. (2008), "Automated modal parameter estimation by parallel processing within wireless monitoring systems", J. Infrastruct. Syst., 14(1), 102-113. https://doi.org/10.1061/(ASCE)1076-0342(2008)14:1(102)
  9. Mascarenas, D.D.L., Flynn, E.B., Todd, M.D., Overly, T.G., Farinholt, K.M., Park, G. and Farrar, C.R. (2010), "Development of capacitance-based and impedance-based wireless sensors and sensor nodes for structural health monitoring applications", J. Sound Vib., 329(12), 2410-2420. https://doi.org/10.1016/j.jsv.2009.07.021
  10. Boller, C. (2000), "Next generation structural health monitoring and its integration into aircraft design", Int. J. Syst. Sci., 31(11), 1333-49. https://doi.org/10.1080/00207720050197730
  11. Hu, N., Wang, X., Fukunaga, H., Yao, Z.H., Zhang, H.X. and Wu, Z.S. (2001), "Damage assessment of structures using modal test data", Int. J. Solids Struct., 38(18), 3111-3126. https://doi.org/10.1016/S0020-7683(00)00292-4
  12. Johnson, T.J., Brown, R.L., Adams, D.E. and Schiefer, M. (2004), "Distributed structural health monitoring with a smart sensor array", Mech. Syst. Signal Pr., 18(3), 555-72. https://doi.org/10.1016/S0888-3270(03)00002-5
  13. Hera, A. and Hou, Z. (2004), "Application of wavelet approach for ASCE structural health monitoring benchmark studies", J. Eng. Mech., 130(1), 96-104. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(96)
  14. Udwadia, F.E. and Proskurowski, W. (1998), "A memory-matrix-based identification methodology for structural and mechanical systems", Earthq. Eng. Struct. D., 27(12), 1465-1481. https://doi.org/10.1002/(SICI)1096-9845(199812)27:12<1465::AID-EQE795>3.0.CO;2-7
  15. Zhang, J., Sato, T. and Iai, S. (2006), "Support vector regression for on-line health monitoring of large-scale structures", Struct. Safety, 28(4), 392-406. https://doi.org/10.1016/j.strusafe.2005.12.001
  16. Carden, E.P. and Brownjohn, J.M.W. (2008), "ARMA modelled time-series classification for structural health monitoring of civil infrastructure", Mech. Syst. Signal Pr., 22(2), 295-314. https://doi.org/10.1016/j.ymssp.2007.07.003
  17. Sohn, H., Czarnecki, J.A. and Farrar, C.R. (2000), "Structural health monitoring using statistical process control", J. Struct. Eng., 126(11), 1356-1363. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:11(1356)
  18. Sohn, H. and Farrar, C.R. (2001), "Damage diagnosis using time series analysis of vibration signals", Smart Mater. Struct., 10(3), 446-51. https://doi.org/10.1088/0964-1726/10/3/304
  19. Ljung, L. (1986), "System identification: theory for the user", Prent-Hall, Inc. Upper Saddle River, NJ, USA.
  20. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D. and Lander, E.S. (1999), "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring", Science, 286(5439), 531-537. https://doi.org/10.1126/science.286.5439.531
  21. Donna, K.S., Pablo, T., Jill, P.M., Todd, R.G. and Eric, S.L. (2000), "Class prediction and discovery using gene expression data", Annual Conference on Research in Computational Molecular Biology, Proceedings of the 4th annual international conference on Computational molecular biology, 263-272, 1-58113-186-0, Tokyo.

Cited by

  1. Data fusion approaches for structural health monitoring and system identification: Past, present, and future pp.1741-3168, 2018, https://doi.org/10.1177/1475921718798769