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Robust proportional hazard-based monitoring schemes for reliability data
Quality and Reliability Engineering International ( IF 2.3 ) Pub Date : 2021-05-20 , DOI: 10.1002/qre.2921
Moezza Nabeel 1 , Sajid Ali 1 , Ismail Shah 1
Affiliation  

Reliability of products is a key factor for successful businesses. In general, the existing monitoring schemes have poor performance as reliability data are often censored. Also, the products are manufactured in multistage processes where the outgoing quality gets affected by the previous stage quality. Besides this cascade property, historical data with outliers make the analysis even more complicated. This paper discusses exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts for monitoring a two-stage dependent process. In particular, the proportional hazard (PH) model is assumed for modeling the relationship of quality characteristics. Furthermore, to remove the detrimental effects of outliers on the results, robust regression method known as the M-estimation is used. Besides a real case study, the performance of the proposed monitoring approach is assessed through a comprehensive simulation study. The results suggested that the EWMA chart outperforms the CUSUM chart.

中文翻译:

用于可靠性数据的稳健的基于比例风险的监测方案

产品的可靠性是成功企业的关键因素。一般来说,由于可靠性数据经常被删失,现有的监测方案性能不佳。此外,产品是在多阶段过程中制造的,其中出厂质量受到前一阶段质量的影响。除了这种级联属性之外,带有异常值的历史数据使分析变得更加复杂。本文讨论了用于监控两阶段相关过程的指数加权移动平均 (EWMA) 和累积总和 (CUSUM) 控制图。特别是,假设比例风险 (PH) 模型用于对质量特性的关系进行建模。此外,为了消除异常值对结果的不利影响,使用了称为 M 估计的稳健回归方法。除了一个真实的案例研究,提议的监测方法的性能通过综合模拟研究进行评估。结果表明 EWMA 图优于 CUSUM 图。
更新日期:2021-05-20
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