IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v133y2015icp223-236.html
   My bibliography  Save this article

Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

Author

Listed:
  • An, Dawn
  • Kim, Nam H.
  • Choi, Joo-Ho

Abstract

This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available.

Suggested Citation

  • An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
  • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:223-236
    DOI: 10.1016/j.ress.2014.09.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832014002245
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    3. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    4. repec:eee:reensy:v:115:y:2013:i:c:p:161-169 is not listed on IDEAS
    5. Yan, Jie & Liu, Yongqian & Han, Shuang & Qiu, Meng, 2013. "Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 613-621.
    6. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    7. repec:eee:csdana:v:56:y:2012:i:12:p:4215-4228 is not listed on IDEAS
    8. repec:eee:reensy:v:91:y:2006:i:10:p:1390-1397 is not listed on IDEAS
    9. repec:taf:uiiexx:v:45:y:2013:i:4:p:422-435 is not listed on IDEAS
    10. Walter R. Gilks & Carlo Berzuini, 2001. "Following a moving target-Monte Carlo inference for dynamic Bayesian models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 127-146.
    11. repec:eee:reensy:v:95:y:2010:i:1:p:49-57 is not listed on IDEAS
    12. repec:eee:reensy:v:111:y:2013:i:c:p:217-231 is not listed on IDEAS
    13. Brahim-Belhouari, Sofiane & Bermak, Amine, 2004. "Gaussian process for nonstationary time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 705-712, November.
    14. Khosravi, Abbas & Nahavandi, Saeid & Creighton, Doug, 2013. "Quantifying uncertainties of neural network-based electricity price forecasts," Applied Energy, Elsevier, vol. 112(C), pages 120-129.
    15. Huiyan Sang & Jianhua Z. Huang, 2012. "A full scale approximation of covariance functions for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 111-132, January.
    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. repec:eee:rensus:v:81:y:2018:i:p2:p:1917-1925 is not listed on IDEAS
    2. repec:eee:reensy:v:172:y:2018:i:c:p:25-35 is not listed on IDEAS
    3. repec:eee:reensy:v:175:y:2018:i:c:p:225-233 is not listed on IDEAS

    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:133:y:2015:i:c:p:223-236. See general information about how to correct material in RePEc.

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

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.