A new algorithm for prognostics using Subset Simulation
A new algorithm for prognostics using Subset Simulation
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DOI：
10.1016/j.ress.2017.05.042
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
M. Chiachío;J. Chiachío;S. Sankararaman;K. Goebel;J. Andrews
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文献类型：
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作者：
M. Chiachío;J. Chiachío;S. Sankararaman;K. Goebel;J. Andrews
这项工作通过将基于粒子滤波器的预测原理与子集模拟技术相结合，提出了一种有效的预测计算框架，该技术首先在 S.K. Au 和 J.L. Beck [概率工程。 Mech., 16 (2001), pp. 263-277]，其已被命名为 PFP-SubSim。 PFP-SubSim 算法背后的思想是将多步提前预测轨迹分成过程各个阶段选定样本的多个分支，这些分支对应于越来越接近的临界阈值。 在理论发展、讨论和说明性示例证明其功效之后，我们报告了使用该算法在使用结构健康监测数据的碳纤维复合材料试件疲劳损伤传播的挑战性应用中预测寿命终止和剩余使用寿命的经验。 结果表明，PFP-SubSim 算法在计算效率方面优于传统的基于粒子滤波器的预测方法，同时在预测估计中实现相同或更好的准确度测量。 实验还表明，PFP-SubSim 算法在处理稀有事件模拟时具有最高的效率。
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 theend-of-lifeandremaining useful lifein 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.