Comparison study on general methods for modeling lifetime data with covariates
Comparison study on general methods for modeling lifetime data with covariates
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DOI：
10.1109/phm.2017.8079122
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
H. Liao;Samira Karimi
中科院分区：
文献类型：
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作者：
H. Liao;Samira Karimi
伴随协变量的寿命数据（如温度、湿度和电流）在科学和工程中很常见。加速寿命试验（ALT）数据就是一个重要的例子。在ALT中，产品的测试单元暴露在比正常情况更严重的条件下，以加速产品故障。所得到的带有协变量的寿命和/或审查数据通常由带有寿命-压力关系的概率分布来建模。然而，如果选择的概率分布和寿命-应力关系不能充分描述潜在的失效过程，则得到的可靠性预测将具有误导性。本文提出了一种利用相型（PH）分布和一般寿命-应力关系公式对寿命数据进行协变量建模的新方法。通过数值研究比较了该方法与混合威布尔分布模型的性能。这种通用方法为数据生成机制未知或难以使用现有参数化ALT模型和统计工具分析的情况下，使用协变量建模和分析寿命数据开辟了新的方向。
Lifetime data with covariates (e.g., temperature, humidity, and electric current) are frequently seen in science and engineering. An important example is accelerated life testing (ALT) data. In ALT, test units of a product are exposing to severer-than-normal conditions to expedite product failure. The resulting lifetime and/or censoring data with covariates are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. This paper develops a new method for modeling lifetime data with covariates using phase-type (PH) distributions and a general life-stress relationship formulation. A numerical study is presented to compare the performance of this method with a mixture of Weibull distributions model. This general method creates a new direction for modeling and analyzing lifetime data with covariates for situations where the data-generating mechanisms are unknown or difficult to analyze using existing parametric ALT models and statistical tools.