IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v94y2009i2p180-186.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832008000471
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2008.02.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Marseguerra M. & Zio E., 1998. "Weight updating in forced Monte Carlo approach to dynamic PSA," Monte Carlo Methods and Applications, De Gruyter, vol. 4(4), pages 359-374, December.
    3. F Cadini & D Avram & E Zio, 2010. "System state estimation by particle filtering for fault diagnosis and prognosis," Journal of Risk and Reliability, , vol. 224(3), pages 149-158, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:180-186. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.