DOI QR코드

DOI QR Code

Spurious mode distinguish by eigensystem realization algorithm with improved stabilization diagram

  • Qu, Chun-Xu (School of Civil Engineering, Dalian University of Technology) ;
  • Yi, Ting-Hua (School of Civil Engineering, Dalian University of Technology) ;
  • Yang, Xiao-Mei (School of Civil Engineering, Dalian University of Technology) ;
  • Li, Hong-Nan (School of Civil Engineering, Dalian University of Technology)
  • 투고 : 2016.11.16
  • 심사 : 2017.05.01
  • 발행 : 2017.09.25

초록

Modal parameter identification plays a key role in the structural health monitoring (SHM) for civil engineering. Eigensystem realization algorithm (ERA) is one of the most popular identification methods. However, the complex environment around civil structures can introduce the noises into the measurement from SHM system. The spurious modes would be generated due to the noises during ERA process, which are usually ignored and be recognized as physical modes. This paper proposes an improved stabilization diagram method in ERA to distinguish the spurious modes. First, it is proved that the ERA can be performed by any two Hankel matrices with one time step shift. The effect of noises on the eigenvalues of structure is illustrated when the choice of two Hankel matrices with one time step shift is different. Then, a moving data diagram is proposed to combine the traditional stabilization diagram to form the improved stabilization diagram method. The moving data diagram shows the mode variation along the different choice of Hankel matrices, which indicates whether the mode is spurious or not. The traditional stabilization diagram helps to determine the concerned truncated order before moving data diagram is implemented. Finally, the proposed method is proved through a numerical example. The results show that the proposed method can distinguish the spurious modes.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

참고문헌

  1. Au, S.K. and Zhang, F.L. (2016), "Fundamental two-stage formulation for Bayesian system identification, part I: general theory", Mech. Syst. Signal. Pr., 66, 31-42.
  2. Bazan, F.S.V. (2004), "Eigensystem realization algorithm (ERA): reformulation and system pole perturbation analysis", J. Sound Vib., 274(1), 433-444. https://doi.org/10.1016/j.jsv.2003.09.037
  3. Cara, F.J., Carpio, J., Juan, J. and Alarcon, E. (2012), "An approach to operational modal analysis using the expectation maximization algorithm", Mech. Syst. Signal. Pr., 31, 109-129. https://doi.org/10.1016/j.ymssp.2012.04.004
  4. Chen, H.P. and Maung, T.S. (2014), "Regularised finite element model updating using measured incomplete modal data", J. Sound Vib., 333(21), 5566-5582. https://doi.org/10.1016/j.jsv.2014.05.051
  5. Ibraham, S.R. (2001), "Efficient random decrement computation for identification of ambient responses", Proceedings of the 19th IMAC, Orlando, FL.
  6. James, G.H., Carne, T.G. and Lauffer, J.P. (1995), "The natural excitation technique (NExT) for modal parameter extraction from operating structures", Modal. Anal., 10(4), 260-277.
  7. Jeffrey, B.B. (1998), Linear optimal control: H2 and H-infinity methods, Addison-Wesley Longman Publishing Co., Inc.
  8. Juang, J.N. and Pappa, R.S. (1985), "An eigensystem realization algorithm for modal parameter identification and model reduction", J. Guid. Control. Dyn., 8(5), 620-627. https://doi.org/10.2514/3.20031
  9. Lei, Y., Sohn, H. and Yi, T.H. (2014), "Advances in monitoring-based structural identification, damage detection and condition assessment", Struct. Stab. Dyn., 14(5).
  10. Li, H.N., Qu, C.X., Huo, L. and Nagarajaiah, S. (2016), "Equivalent bilinear elastic single degree of freedom system of multi-degree of freedom structure with negative stiffness", J. Sound Vib., 365, 1-14. https://doi.org/10.1016/j.jsv.2015.11.005
  11. Magalhaes, F., Cunha, A. and Caetano, E. (2009), "Online automatic identification of the modal parameters of a long span arch bridge", Mech. Syst. Signal. Pr., 23(2), 316-329. https://doi.org/10.1016/j.ymssp.2008.05.003
  12. Marchesiello, S., Fasana, A. and Garibaldi, L. (2016), "Modal contributions and effects of spurious poles in nonlinear subspace identification", Mech. Syst. Signal. Pr., 74, 111-132. https://doi.org/10.1016/j.ymssp.2015.05.008
  13. Peeters, B. and Roeck, G.D. (2001), "Stochastic system identification for operational modal analysis: a review", J. Dyn. Syst., 123(4), 659-667. https://doi.org/10.1115/1.1410370
  14. Qu, C.X., Li, H.N., Huo, L. and Yi, T.H. (2017), "Optimum value of negative stiffness and additional damping in civil structures", J. Struct. Eng., 04017068.
  15. Van Overschee, P. and De Moor, B.L. (2012), Subspace Identification for Linear Systems: Theory-Implementation-Applications, Springer Science & Business Media.
  16. Verboven, P., Parloo, E., Guillaume, P. and Overmeire, M.V. (2002), "Autonomous structural health monitoring-part I: modal parameter estimation and tracking", Mech. Syst. Signal. Pr., 16(4), 637-657. https://doi.org/10.1006/mssp.2002.1492
  17. Yi, T.H., Li, H.N. and Gu, M. (2011), "A new method for optimal selection of sensor location on a high-rise building using simplified finite element model", Struct. Eng. Mech., 37(6), 671-684. https://doi.org/10.12989/sem.2011.37.6.671
  18. Yi, T.H., Li, H.N. and Zhang, X.D. (2012), "Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm", Smart Mater. Struct., 21(12), 125023. https://doi.org/10.1088/0964-1726/21/12/125023
  19. Zhang, F.L. and Au, S.K. (2016), "Fundamental two-stage formulation for Bayesian system identification, part II: application to ambient vibration data", Mech. Syst. Signal. Pr., 66, 43-61.

피인용 문헌

  1. Modal identification for superstructure using virtual impulse response vol.22, pp.16, 2019, https://doi.org/10.1177/1369433219862951
  2. Seismic Performance and Strengthening of Purlin Roof Structures Using a Novel Damping-Limit Device vol.8, pp.None, 2021, https://doi.org/10.3389/fmats.2021.722018
  3. Complex frequency identification using real modal shapes for a structure with proportional damping vol.36, pp.10, 2021, https://doi.org/10.1111/mice.12676