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Prediction of Future Failures for Heterogeneous Reliability Field Data
Technometrics ( IF 2.5 ) Pub Date : 2021-06-07 , DOI: 10.1080/00401706.2021.1921036
Colin Lewis-Beck 1 , Qinglong Tian 2 , William Q. Meeker 2
Affiliation  

Abstract

This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to predict future failures from the same process. Early data from high-reliability products, however, often have limited information due to some combination of small sample sizes, censoring, and truncation. In such cases, we use a Bayesian hierarchical model to model jointly multiple lifetime distributions arising from different subpopulations of similar products. By borrowing information across subpopulations, our method enables stable estimation and the computation of corresponding prediction intervals, even in cases where there are few observed failures. Three applications are provided to illustrate this methodology, and a simulation study is used to validate the coverage performance of the prediction intervals.



中文翻译:

预测异构可靠性现场数据的未来故障

摘要

本文介绍了从异构可靠性现场数据构建未来故障数量的预测界限或区间的方法。我们专注于样本内预测,其中来自故障时间过程的早期数据用于预测同一过程的未来故障。然而,由于样本量小、审查和截断的某些组合,来自高可靠性产品的早期数据通常信息有限。在这种情况下,我们使用贝叶斯层次模型来联合模拟由相似产品的不同亚群产生的多个寿命分布。通过跨亚群借用信息,我们的方法可以实现稳定的估计和相应预测区间的计算,即使在观察到的故障很少的情况下也是如此。

更新日期:2021-06-07
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