Decentralized Modal Analysis and System Identification Using Embedded Markov Parameter Extraction within Distributed Wireless Sensor Networks

  • Junhee Kim
  • Jerome P. Lynch
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


As wireless monitoring systems continue to mature as a viable alternative to traditional wired data acquisition systems, scalable approaches to autonomously processing measurement data in-network are necessary. Embedded data processing has the benefit of improving system scalability, reducing the amount of wireless communications, and reducing overall power consumption. A system identification strategy based on Markov parameters is proposed for embedment within the decentralized computational framework of a wireless sensor network. Utilizing the computational resources of wireless sensors, individual sensor nodes perform local data processing to identify the Markov parameters of a structural system. Eventually, the global structural properties (e.g., mode shapes) are assembled by the wireless sensor network base station via an eigensystem realization algorithm executed using the limited number of Markov parameters transmitted by the wireless sensor nodes. The proposed strategy is evaluated using input-output and output-only data recorded during dynamic testing of a balcony structure.


Sensor Node Wireless Sensor Network Wireless Sensor Node Structural Health Monitoring System System Identification Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Straser, E.G. and A.S. Kiremidjian, A Modular, Wireless Damage Monitoring System for Structures. 1998, John A. Blume Earthquake Engineering Center,Stanford University: Stanford, CA.Google Scholar
  2. 2.
    Lynch, J.P., Y. Wang, K.J. Loh, J.H. Yi, and C.B. Yun, Performance Monitoring of The Geumdang Bridge Using a Dense Network of High-resolution Wireless Sensors. Smart Materials and Structures, 2006. 15(6): p. 1561–1575.CrossRefGoogle Scholar
  3. 3.
    Juang, J.N., M. Phan, and L.G.L. Horta, R.W., Identification of Observer/Kalman Filter Markov Parameters: Theory and Experiment. 1991, Langley Research Center: Hampton, Virginia.Google Scholar
  4. 4.
    James, J.H., T.G. Carne, and J.P. Lauffer, The Natural Excitation Technique (NExT) for Modal Parameter Extraction from Operating Structures. The International Journal of Analytical and Experimental Modal Analysis, 1996. 10(4): p. 260–277.Google Scholar
  5. 5.
    Van Pelt, T.H. and D.S. Bernstein. Least Squares Identification Using μ-Markov Parameterizations. in 37th IEEE Conference on Decision & Control. 1998. Tampa, FL.Google Scholar
  6. 6.
    Holzel, M.S. and D.S. Bernstein. On the Accuracy of Least Squares Estimations of Markov Parameters Based on μ-Markov Models. in 15th International Federation of Automatic Control Symposium on System Identification (IFAC SYSID). 2009. Saint-Malo, France.Google Scholar
  7. 7.
    Ho, B.L. and R.E. Kalman, Effective Construction of Linear State-variable Models from Input-Output Functions. Regelungtechnik, 1965. 12: p. 545–548.Google Scholar
  8. 8.
    Viberg, M., Subspace-based Methods for the Identification of Linear Time-invariant Systems. Automatica, 1995. 31(12): p. 1835–1851.MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Van Overschee, P. and B. De Moor, Subspace Identification for Linear Systems. 1996, Dordrecht, Netherlands: Kluwer Academic Publishers.MATHCrossRefGoogle Scholar
  10. 10.
    Juang, J.N. and R.S. Pappa, An Eigensystem Realization Algorithm for Modal Parameter Identification and Model Reduction. Journal of Guidance, 1984. 8(5): p. 620–627.CrossRefGoogle Scholar
  11. 11.
    Swartz, R.A., D. Jung, J.P. Lynch, Y. Wang, D. Shi, and M.P. Flynn. Design of a Wireless Sensor for Scalable Distributed In-Network Computation in a Structural Health Monitoring System. in 5th International Workshop on Structural Health Monitoring. 2005. Palo Alto, CA.Google Scholar
  12. 12.
    Kim, J., R.A. Swartz, J.P. Lynch, J.J. Lee, and C.G. Lee, Extended-range Narada wireless sensors for rapid reconfigurable vibration monitoring of highway bridges. Smart Structures and Systems, 2010. 6(5–6).Google Scholar
  13. 13.
    Wang, Y., J.P. Lynch, and K.H. Law, A Wireless Structural Health Monitoring System With Multithreaded Sensing Devices: Design and Validation. Structural and Infrastructure Engineering, 2007. 3(2): p. 103–120.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2011

Authors and Affiliations

  • Junhee Kim
    • 1
  • Jerome P. Lynch
    • 1
  1. 1.Department of Civil and Environmental EngineeringUniversity of MichiganAnn ArborUSA

Personalised recommendations