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Monte Carlo simulation for model-based fault diagnosis in dynamic systems

Author

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  • Marseguerra, Marzio
  • Zio, Enrico

Abstract

Fault diagnosis requires the accurate estimation of the dynamic state of the system in real time. This can be pursued starting from a model of the system dynamics and on measurements related to the state of the system. In real applications, the nonlinearity of the model and non-Gaussianity of the noise typically affecting the measurement challenge the classical approximate approaches, e.g. the extended-Kalman, Gaussian-sum and grid-based filters, which often turn out to be inaccurate and/or too computationally expensive for real-time applications. On the contrary, Monte Carlo estimation methods, also called particle filters, can be very effective. Based on sequential importance sampling and on a Bayesian formulation of the estimation problem, these methods recursively approximate the relevant probability distributions of the system state by random measures composed of particles (sampled values of the unknown state variables) and associated weights.

Suggested Citation

  • Marseguerra, Marzio & Zio, Enrico, 2009. "Monte Carlo simulation for model-based fault diagnosis in dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 180-186.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:180-186
    DOI: 10.1016/j.ress.2008.02.013
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    References listed on IDEAS

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    1. Labeau, P.E & Zio, E, 1998. "The cell-to-boundary method in the frame of memorization-based Monte Carlo algorithms. A new computational improvement in dynamic reliability," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 347-360.
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    Cited by:

    1. Baraldi, Piero & Mangili, Francesca & Zio, Enrico, 2013. "Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 94-108.
    2. Gayathri, P. & Umesh, K. & Ganguli, R., 2010. "Effect of matrix cracking and material uncertainty on composite plates," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 716-728.
    3. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).

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