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Application of Bayesian Inference to Unbalance Identification in Rotors

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Proceedings of the 9th IFToMM International Conference on Rotor Dynamics

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 21))

Abstract

This work proposes the use of Bayesian inference to fault parameters identification taking into account the stochastic characteristic of the system. The objective is to estimate the unbalance parameters, as the unbalance moment, phase angle and axial position of the unbalance force applied to the rotor. Therefore, experimental tests with the rotor to obtain the unbalance response are performed. The statistical distribution of each parameter is obtained using a Markov Chain Monte Carlo method (MCMC), simulated with the Delayed Rejection Adaptive Metropolis algorithm (DRAM). Thus, the residual between experimental and numerical response is calculated and applied to a Bayesian inference analysis, obtaining information about the unbalance parameters, which are summarized in statistics for each parameter distributions.

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Acknowledgments

The authors would like to thank FAPESP, CNPq, CAPES and Research office of UNICAMP for supporting this research.

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Authors and Affiliations

  1. Laboratory of Rotating Machinery, Faculty of Mechanical Engineering, University of Campinas – UNICAMP, 6122, Campinas, SP, 13083-970, Brazil

    Natalia Cezaro Tyminski & Helio Fiori de Castro

Authors
  1. Natalia Cezaro Tyminski
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  2. Helio Fiori de Castro
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Corresponding author

Correspondence to Natalia Cezaro Tyminski .

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Editors and Affiliations

  1. Mechanical Engineering, Politecnico di Milano, Milano, Italy

    Paolo Pennacchi

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Tyminski, N.C., de Castro, H.F. (2015). Application of Bayesian Inference to Unbalance Identification in Rotors. In: Pennacchi, P. (eds) Proceedings of the 9th IFToMM International Conference on Rotor Dynamics. Mechanisms and Machine Science, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-06590-8_58

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  • DOI: https://doi.org/10.1007/978-3-319-06590-8_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06589-2

  • Online ISBN: 978-3-319-06590-8

  • eBook Packages: EngineeringEngineering (R0)

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