A new algorithm for prognostics using subset simulation
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-SubSi...
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/43419/ |
| _version_ | 1848796683735924736 |
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| author | Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Goebel, Kai Andrews, John |
| author_facet | Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Goebel, Kai Andrews, John |
| author_sort | Chiachío, Manuel |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:51:53Z |
| format | Article |
| id | nottingham-43419 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:51:53Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-434192020-05-04T19:53:49Z https://eprints.nottingham.ac.uk/43419/ A new algorithm for prognostics using subset simulation Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Goebel, Kai Andrews, John 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. Elsevier 2017-12 Article PeerReviewed 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 Prognostics; Rare events; Stochastic modeling; Subset Simulation http://www.sciencedirect.com/science/article/pii/S0951832016307335 doi:10.1016/j.ress.2017.05.042 doi:10.1016/j.ress.2017.05.042 |
| spellingShingle | Prognostics; Rare events; Stochastic modeling; Subset Simulation Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Goebel, Kai Andrews, John A new algorithm for prognostics using subset simulation |
| title | A new algorithm for prognostics using subset simulation |
| title_full | A new algorithm for prognostics using subset simulation |
| title_fullStr | A new algorithm for prognostics using subset simulation |
| title_full_unstemmed | A new algorithm for prognostics using subset simulation |
| title_short | A new algorithm for prognostics using subset simulation |
| title_sort | new algorithm for prognostics using subset simulation |
| topic | Prognostics; Rare events; Stochastic modeling; Subset Simulation |
| url | https://eprints.nottingham.ac.uk/43419/ https://eprints.nottingham.ac.uk/43419/ https://eprints.nottingham.ac.uk/43419/ |