A new algorithm for prognostics using subset simulation

Chiachío, Manuel, Chiachío, Juan, Sankararaman, Shankar, Goebel, Kai and Andrews, John (2017) A new algorithm for prognostics using subset simulation. Reliability Engineering & System Safety, 168 . pp. 189-199. ISSN 0951-8320

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/964183
Keywords: Prognostics; Rare events; Stochastic modeling; Subset Simulation
Schools/Departments: University of Nottingham, UK > Faculty of Engineering
Identification Number: https://doi.org/10.1016/j.ress.2017.05.042
Depositing User: Eprints, Support
Date Deposited: 06 Jun 2017 14:49
Last Modified: 04 May 2020 19:53
URI: https://eprints.nottingham.ac.uk/id/eprint/43419

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