A new algorithm for prognostics using subset simulation

Chiach�o, Manuel; Chiach�o, Juan; Sankararaman, Shankar; Goebel, Kai; Andrews, John

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.

Citation

Chiachío, M., Chiachío, J., Sankararaman, S., Goebel, K., & Andrews, J. (2017). A new algorithm for prognostics using subset simulation. Reliability Engineering and System Safety, 168, https://doi.org/10.1016/j.ress.2017.05.042

• Journal Article Type: Article
• Acceptance Date: May 27, 2017
• Online Publication Date: Jun 2, 2017
• Publication Date: Dec 1, 2017
• Deposit Date: Jun 6, 2017
• Publicly Available Date: Jun 6, 2017
• Journal: Reliability Engineering & System Safety
• Electronic ISSN: 0951-8320
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 168
• DOI: https://doi.org/10.1016/j.ress.2017.05.042
• Keywords: Prognostics; Rare events; Stochastic modeling; Subset Simulation
• Public URL: https://nottingham-repository.worktribe.com/output/964183
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0951832016307335