Skip to main content
SpringerLink
Log in

Modal Parameter Identification of a Quayside Container Crane Based on Data-Driven Stochastic Subspace Identification

  • Original Paper
  • Published:
Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Background

Modal parameters of a quayside container crane can reflect the vibration performance of a structure at work. Identifying these parameters through modal tests can be used to improve the design scheme, evaluate performance, and detect damage.

Method

This paper introduces the process of modal parameter identification based on data-driven stochastic subspace identification (SSI). The proposed algorithm is programmed based on the MATLAB platform. The effectiveness of the algorithm is verified by a numerical three-degree-of-freedom vibration model. In this paper, the overall structure of a quayside container crane was modally tested for the first time under ambient excitation. The test scheme using the mobile measurement method is introduced in detail. The signal data collected by the modal test are used as the input data of the data-driven SSI, and the first seven natural frequencies, damping ratios, and mode shapes of the crane are identified successfully. Furthermore, the finite element model of the crane is established with ANSYS, and the first seven frequencies and mode shapes are also obtained.

Results and Conclusions

By comparing the results of the two methods, it can be found that the parameters identified by the proposed algorithm have high accuracy. The proposed method can be applied to the health monitoring system of the quayside container crane.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Feau C, Politopoulos I, Kamaris GS, Mathey C, Chaudat T, Nahas G (2015) Experimental and numerical investigation of the earthquake response of crane bridges. Eng Struct 84:89–101. https://doi.org/10.1016/j.engstruct.2014.11.022

    Article  Google Scholar 

  2. Peeters B, De Roeck G (2001) One-year monitoring of the Z24-Bridge: environmental effects versus damage events. Earthq Eng Struct Dynam 30:149–171. https://doi.org/10.1002/1096-9845(200102)30:2%3c149::Aid-eqe1%3e3.0.Co;2-z

    Article  Google Scholar 

  3. Grande E, Imbimbo M (2012) A data-driven approach for damage detection: an application to the ASCE steel benchmark structure. J Civil Struct Health Monit 2:73–85. https://doi.org/10.1007/s13349-012-0018-z

    Article  Google Scholar 

  4. Hung CF, Ko WJ (2002) Identification of modal parameters from measured input and output data using a vector backward autoregressive model. J Sound Vib 256:249–270. https://doi.org/10.1006/jsvi.2001.4205

    Article  Google Scholar 

  5. Ren WX, Peng XL, Lin YQ (2005) Experimental and analytical studies on dynamic characteristics of a large span cable-stayed bridge. Eng Struct 27:535–548. https://doi.org/10.1016/j.engstruct.2004.11.013

    Article  Google Scholar 

  6. Kraemer P, Friedmann H (2015) Vibration-based structural health monitoring for offshore wind turbines—experimental validation of stochastic subspace algorithms. Wind Struct 21:693–707. https://doi.org/10.12989/was.2015.21.6.693

    Article  Google Scholar 

  7. Reynders E, Roeck GD (2008) Reference-based combined deterministic–stochastic subspace identification for experimental and operational modal analysis. Mech Syst Signal Process 22:617–637. https://doi.org/10.1016/j.ymssp.2007.09.004

    Article  Google Scholar 

  8. Cho S, Jo H, Jang S, Park J, Jung HJ, Yun CB, Spencer BF, Seo JW (2010) Structural health monitoring of a cable-stayed bridge using wireless smart sensor technology: data analyses. Smart Struct Syst 6:461–480. https://doi.org/10.12989/sss.2010.6.5_6.461

    Article  Google Scholar 

  9. Peeters B, De Roeck G (2001) Stochastic system identification for operational modal analysis: a review. J Dyn Syst Meas Control 123:659–667. https://doi.org/10.1115/1.1410370

    Article  Google Scholar 

  10. Hung CF, Ko WJ, Tai CH (2002) Identification of dynamic systems from data composed by combination of their response components. Eng Struct 24:1441–1450. https://doi.org/10.1016/S0141-0296(02)00092-5

    Article  Google Scholar 

  11. Alicioglu B, Lus H (2008) Ambient vibration analysis with subspace methods and automated mode selection: case studies. J Struct Eng 134:1016–1029. https://doi.org/10.1061/(asce)0733-9445(2008)134:6(1016)

    Article  Google Scholar 

  12. Reynders E, Pintelon R, De Roeck G (2008) Uncertainty bounds on modal parameters obtained from stochastic subspace identification. Mech Syst Signal Process 22:948–969. https://doi.org/10.1016/j.ymssp.2007.10.009

    Article  Google Scholar 

  13. Li WC, Vu VH, Liu ZH, Thomas M, Hazel B (2018) Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification. J Vib Control 24:4781–4796. https://doi.org/10.1177/1077546317734670

    Article  MathSciNet  Google Scholar 

  14. Brownjohn JMW, Magalhaes F, Caetano E, Cunha A (2010) Ambient vibration re-testing and operational modal analysis of the Humber Bridge. Eng Struct 32:2003–2018. https://doi.org/10.1016/j.engstruct.2010.02.034

    Article  Google Scholar 

  15. Overschee PV, Moor BD (1991) Subspace algorithms for the stochastic identification problem. In: Proceedings of the 30th IEEE conference on decision and control, 11–13 Dec. 1991, vol 1322, pp 1321–1326. https://doi.org/10.1109/CDC.1991.261604

  16. Peeters B, De Roeck G (1999) Reference-based stochastic subspace identification for output-only modal analysis. Mech Syst Signal Process 13:855–878. https://doi.org/10.1006/mssp.1999.1249

    Article  Google Scholar 

  17. Priori C, De Angelis M, Betti R (2018) On the selection of user-defined parameters in data-driven stochastic subspace identification. Mech Syst Signal Process 100:501–523. https://doi.org/10.1016/j.ymssp.2017.07.045

    Article  Google Scholar 

  18. Van Overschee P, De Moor B (1996) Subspace identification. Subspace identification for linear systems: theory—implementation—applications. Springer, Boston, pp 57–93. https://doi.org/10.1007/978-1-4613-0465-4_3

    Chapter  MATH  Google Scholar 

  19. Peeters B (2000) System identification and damage detection in civil engineering. Katholieke Universiteit Leuven, Belgium.

  20. Bakir PG (2011) Automation of the stabilization diagrams for subspace based system identification. Expert Syst Appl 38:14390–14397. https://doi.org/10.1016/j.eswa.2011.04.021

    Article  Google Scholar 

  21. Pappa R, James HG, Zimmerman DC (1997) Autonomous modal identification of the space shuttle tail rudder. J Spacecr Rockets. https://doi.org/10.2514/2.3324

    Article  Google Scholar 

  22. Yuan K, Zhu W (2020) Modeling of welded joints in a pyramidal truss sandwich panel using beam and shell finite elements. J Vib Acoust 143(4): 041002. https://doi.org/10.1115/1.4048792

  23. Li J, Zhu X, Law S-s, Samali B (2019) Indirect bridge modal parameters identification with one stationary and one moving sensors and stochastic subspace identification. J Sound Vib 446:1–21. https://doi.org/10.1016/j.jsv.2019.01.024

    Article  Google Scholar 

  24. Döhler M, Andersen P, Mevel L (2011) Data merging for multi-setup operational modal analysis with data-driven SSI. Structural dynamics, vol 3. Springer, New York, pp 443–452

    Google Scholar 

  25. Tong M, Wang Y, Qiu H (2011) Dynamic responses of high speed quay container cranes. Proc Eng 16:342–347

    Article  Google Scholar 

  26. Azeloglu CO, Ozen S, Edincliler A, Kenan H (2017) Natural frequency analysis of lattice boom crane theoretically and experimentally. Int J Steel Struct 17:757–762. https://doi.org/10.1007/s13296-017-6029-1

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China

    Xiuzhong Xu & Xiancheng Gu

  2. School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xu Zhang

  3. Department of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA

    Weidong Zhu

Authors
  1. Xiuzhong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weidong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiancheng Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiuzhong Xu or Xu Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, X., Zhang, X., Zhu, W. et al. Modal Parameter Identification of a Quayside Container Crane Based on Data-Driven Stochastic Subspace Identification. J. Vib. Eng. Technol. 9, 919–938 (2021). https://doi.org/10.1007/s42417-020-00273-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42417-020-00273-8

Keywords

Search

Navigation