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Simulation-based Bayesian optimal design for multi-factor accelerated life tests

机译:基于仿真的贝叶斯优化设计用于多因素加速寿命测试

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We consider simulation-based methods for the design of multi-stress factor accelerated life tests (ALTs) in a Bayesian decision theoretic framework. Multi-stress factor ALTs are challenging due to the increased number of simulation runs required as a result of stress factor-level combinations. We propose the use of Latin hypercube sampling to reduce the simulation cost without loss of statistical efficiency. Exploration and optimization of expected utility function is carried out by a developed algorithm that utilizes Markov chain Monte Carlo methods and nonparametric smoothing techniques. A comparison of proposed approach to a full grid simulation is provided to illustrate computational cost reduction.
机译:我们在贝叶斯决策理论框架中考虑基于仿真的方法来设计多应力因子加速寿命试验(ALT)。多应力因子ALTs具有挑战性,因为应力因子水平组合导致所需的模拟运行次数增加。我们建议使用拉丁超立方体采样来减少模拟成本,而又不损失统计效率。通过使用马尔可夫链蒙特卡罗方法和非参数平滑技术的已开发算法对预期效用函数进行探索和优化。比较了所提出的方法与全网格模拟,以说明计算成本的降低。

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