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Estimation Method for Extremely Small Sample Accelerated Degradation Test Data

机译：极小样品加速降解测试数据的估计方法

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For the purpose of assessing the accelerated degradation failure time distribution of product, a kind of method to extend extremely small sample for large sample is put forward in this paper. This method divides the original degradation test data into sections and randomly selects partial data from each piece of data. Then, the sampled data form a new test sample. Through this method the sample size could be expended. Considering fixed factors and random factors, the mixed parameter model is adopted to conduct modeling for the degradation paths of the augmented samples. The model parameters are estimated by the two-stage approach. Firstly, each degradation path model parameters are evaluated with least square method. Secondly, the parameters of mixed model are calculated based on the first stage's estimation. The cumulative probability distribution function of product's degradation failure time could be derived through the degradation model. The large sample data of natural storage is used to demonstrate this method. The result shows that using piecewise random sampling method to resampling the accelerated test data can effectively expand the test sample size without generating virtual data and solve the problem of evaluating the failure time distribution of degradation test introduced by insufficient samples.
机译：为了评估产品的加速降解失效时间分布，本文提出了一种延长大型样品的极小样品的方法。该方法将原始劣化测试数据划分为部分，随机选择来自每条数据的部分数据。然后，采样数据形成新的测试样本。通过这种方法，可以消耗样本大小。考虑到固定因素和随机因素，采用混合参数模型进行增强样本的降解路径的建模。模型参数由两阶段方法估算。首先，通过最小二乘法评估每个劣化路径模型参数。其次，基于第一阶段的估计计算混合模型的参数。产品的累积概率分布函数可以通过降级模型来得出。自然存储的大型样本数据用于演示该方法。结果表明，使用分段随机采样方法来重新采样加速的测试数据可以有效地扩展测试样本大小而不产生虚拟数据，并解决通过空气不足引入的降解测试的失效时间分布的问题。

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