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Parameter Identification in a Beam from Experimental Vibration Measurements Using Particle Filtering

  • Bharat PokaleEmail author
  • R. Rangaraj
  • Sayan Gupta
Conference paper

Abstract

The focus of the present study is on the development of a methodology for estimating the parameters of a cantilever beam based on experimentally measured vibration response. A aluminum cantilever beam of known dimensions is excited at a known location and the tip accelerations are measured. Subsequently, a particle-filtering-based strategy is used to estimate the system parameters from the vibration response.

Keywords

System identification Health monitoring Dynamic state estimation Particle filtering Vibrations 

Notes

Acknowledgements

This study has been supported by Aeronautical Development Agency, Govt. of India under the National Program on Micro and Smart Systems (NPMASS).

References

  1. 1.
    Brown RG, Hwang PYC (1992) Introduction to random signals and applied Kalman filtering. Wiley, New YorkzbMATHGoogle Scholar
  2. 2.
    Chaudhari TD, Maiti SK (2000) Experimental verification of a method of detection of crack in taper and segmented beams based on modelling of transverse vibration. Int J Fract 102:L33–L38CrossRefGoogle Scholar
  3. 3.
    Patil DP, Maiti SK (2005) Experimental verification of a method of detection of multiple cracks in beams based on frequency measurements. J Sound Vib 281:439–451CrossRefGoogle Scholar
  4. 4.
    Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to nonlinear/nonGaussian Bayesian state estimation. IEE Proc F 140(2):107–113Google Scholar
  5. 5.
    Nasrellah HA, Manohar CS (2011) Finite element method based Monte Carlo filters for structural system identification. Probab Eng Mech 26(2):294–307CrossRefGoogle Scholar
  6. 6.
    Nasrellah HA, Manohar CS (2011) Particle filters for structural system identification using multiple test and sensor data: a combined computational and experimental study. Struct Control Health Monit 18(1):99–120Google Scholar
  7. 7.
    Kalman RE (1960) A new approach to linear filtering and prediction problems. ASME J Basic Eng 82(D):35–45Google Scholar
  8. 8.
    Myotyri E, Pulkkinen U, Simola K (2006) Application of stochastic filtering for lifetime prediction. Reliab Eng Syst Saf 91:200–208CrossRefGoogle Scholar
  9. 9.
    Rangaraj R, Rao L, Banerjee A, Gupta S (2011) Identification of Fatigue Cracks in vibrating beams using a particle filtering algorithm. National Aerospace seminar on aircraft structures. IIT, KanpurGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Applied MechanicsIndian Institute of Technology MadrasChennaiIndia
  2. 2.Ashok Leyland Technical CenterChennaiIndia

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