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A change-point based reliability prediction model using field return data

机译:使用场返回数据的基于变化点的可靠性预测模型

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In this study, we propose an accurate reliability prediction model for high-volume complex electronic products throughout their warranty periods by using field return data. Our model has a specific application to electronics boards with given case studies using 36-month warranty data. Our model is constructed on a Weibull-exponential hazard rate scheme by using the proposed change point detection method based on backward and forward data analysis. We consider field return data as short-term and long-term corresponding to early failure and useful life phases of the products, respectively. The proposed model is evaluated by applying it to four different board data sets. Each data set has between 1500 and 4000 board failures. Our prediction model can make a 36-month (full warranty) reliability prediction of a board with using its field data as short as 3 months. The predicted results from our model and the direct results using full warranty data match well. This demonstrates the accuracy of our model. We also evaluate our change point method by applying it to our board data sets as well as to a well-known heart transplant data set. (C) 2016 Elsevier Ltd. All rights reserved.
机译:在这项研究中,我们通过使用现场返回数据为大批量复杂电子产品的整个保修期内提出了准确的可靠性预测模型。我们的模型通过使用36个月保修数据的给定案例研究,专门应用于电子板。我们的模型是在Weibull指数风险率方案的基础上,采用基于后向和前向数据分析的拟议变更点检测方法构建的。我们认为现场返回数据是短期的和长期的,分别对应于产品的早期失效和使用寿命。通过将其应用于四个不同的电路板数据集来评估该模型。每个数据集都有1500至4000个板故障。我们的预测模型可以使用3个月的现场数据对一块板进行36个月(完全保修)的可靠性预测。我们模型的预测结果与使用完整保修数据的直接结果非常吻合。这证明了我们模型的准确性。我们还通过将其应用于董事会数据集以及著名的心脏移植数据集来评估变更点方法。 (C)2016 Elsevier Ltd.保留所有权利。

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