A new algorithm for prognostics using Subset Simulation

Manuel Chiachio, Juan Chiachio, Shankar Sankararaman, Kai Goebel, John Andrews

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This work presents an efficient computational framework for prognostics by combining the particle filter-based prognostics principles with the technique of Subset Simulation, first developed in S.K. Au and J.L. Beck [Probabilistic Engrg. Mech., 16 (2001), pp. 263-277], which has been named PFP-SubSim. The idea behind PFP-SubSim algorithm is to split the multi-step-ahead predicted trajectories into multiple branches of selected samples at various stages of the process, which correspond to increasingly closer approximations of the critical threshold. Following theoretical development, discussion and an illustrative example to demonstrate its efficacy, we report on experience using the algorithm for making predictions for the end-of-life and remaining useful life in the challenging application of fatigue damage propagation of carbon-fibre composite coupons using structural health monitoring data. Results show that PFP-SubSim algorithm outperforms the traditional particle filter-based prognostics approach in terms of computational efficiency, while achieving the same, or better, measure of accuracy in the prognostics estimates. It is also shown that PFP-SubSim algorithm gets its highest efficiency when dealing with rare-event simulation.
Original languageEnglish
Pages (from-to)189-199
Number of pages11
JournalReliability Engineering and System Safety
Volume168
Early online date2 Jun 2017
DOIs
Publication statusPublished - 31 Dec 2017

Keywords

  • prognostics
  • rare events
  • stochastic modeling
  • subset simulation

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  • Prizes

    Extraordinary PhD Award

    Juan Chiachio-Ruano (Recipient), Nov 2018

    Prize: Prize (including medals and awards)

  • Cite this

    Chiachio, M., Chiachio, J., Sankararaman, S., Goebel, K., & Andrews, J. (2017). A new algorithm for prognostics using Subset Simulation. Reliability Engineering and System Safety, 168, 189-199. https://doi.org/10.1016/j.ress.2017.05.042